Agomonia LabsAgentic AI Platform Studio
Industry Solutions
Agomonia Labs combines a reusable Agentic AI, LLM, and RAG foundation with domain-specific workflow agents for each industry. The shared platform handles retrieval, orchestration, memory, governance, evaluation, and cloud-native scale, while each vertical adds specialized intelligence for the language, documents, operational systems, personas, and business decisions that matter in that domain. This approach lets organizations move faster from AI experiments to production-ready solutions without rebuilding the entire stack for every new use case.
Clinical visit transcription, SOAP drafts, patient summaries, payer criteria mapping, evidence gaps, submission-risk flags, and review packets.
ADAR sports agents answer cricket questions with live web scraping, latest scorecards, schedules, team strength analysis, player stats, rules, standings, and six-language text/voice conversation.
Food recommendations, menu search, price comparison, Stripe-paid carryout ordering, customer feedback, rating intelligence, voice, and operational workflows for restaurants and fast food.
Upload trainings, meetings, inspections, walkthroughs, sports highlights, product demos, and operational clips; process metadata, frames, transcript signals, captions, timeline chunks, embeddings, and timestamp-aware Q&A.
Clause extraction, obligation tracking, renewal-risk checks, abstract generation, compliance review, and approval workflows.
Tax document classification, income and deduction extraction, financial planning snapshots, net worth and cash-flow signals, missing-item detection, prior-year comparison, CPA/EA packets, advisor review packets, approval, withdrawal, notifications, and audit visibility.
Policy Q&A, invoice and claim evidence retrieval, underwriting document review, audit support, and exception handling.
Listing packets, disclosure review, client document Q&A, contract comparison, transaction checklists, and agent-assistant workflows.
Protocol search, literature grounding, lab report summarization, regulatory evidence packs, and research knowledge assistants.
Geetabitan shows how ADAR can power cultural knowledge assistants with Bengali search, raag/taal filters, notation, voice, and evaluation.
Horizontal Capability Across Industries
Like RAG, Video Intelligence is a reusable foundation that can support many different industries and personas. Trainings, meetings, webinars, inspections, walkthroughs, sports highlights, cultural performances, healthcare education, customer videos, legal recordings, research interviews, and product demos can be processed into searchable chunks with timestamps, transcript signals, frame context, captions, summaries, citations, and chat-ready answers. This lets each vertical reuse the same DocIntel knowledge layer while adding domain-specific agents, review workflows, and business actions on top.
Personas and Problems
Business users should not have to scrub through long recordings to find one decision, one safety moment, one customer signal, one compliance statement, or one training answer. DocIntel makes video searchable and reviewable so teams can ask what happened, when it happened, what evidence supports the answer, and what should be done next.
Healthcare Vertical
DocIntel combines a self-service RAG foundation with specialized healthcare agents for clinical scribe workflows, patient context, after-visit summaries, evidence review, RBAC-controlled collaboration, and prior authorization readiness. The workflow is now end to end: capture the visit, structure the record, review and edit by role, approve, generate patient-ready artifacts, and reuse the saved data for chat and downstream automation.
Record or upload patient-doctor conversations in English, Spanish, Bangla, Hindi, or Arabic, select language, confirm consent, and generate a transcript.
Create SOAP note drafts, patient-friendly summaries, after-visit summaries, follow-up checklists, medications, lab context, care gaps, and administrative flags.
Save visit output and generated after-visit summary artifacts alongside lab reports, medication lists, prior history, referrals, and notes.
Support patient, clinician, and clinician assistant workflows with scoped permissions for review, documentation, follow-up, and collaboration.
Read patient packets and payer policies, map requirements to cited evidence, identify missing documentation, prepare code-review candidates, flag denial risks, generate human-review packets, and track cases through payer decisions.
Upload, OCR, chunk, embed, search, rerank, summarize, compare, cite, and converse across clinical and administrative documents with multilingual voice and chat.
PII redaction, authentication, workspaces, usage limits, traces, audit logs, observability, approval gates, and governance keep each answer inspectable.
Record or upload visits, generate transcripts, draft SOAP notes, prepare patient summaries, create after-visit summaries, extract follow-ups, and save the results as searchable records.
The AVS agent prepares visit reason, clinician impression, medications, tests, referrals, follow-up plan, warning signs, preventive reminders, facility coordination, and patient questions.
Patients can review visit summaries and instructions; clinicians can approve clinical outputs; clinician assistants can prepare documentation, follow-ups, and draft packets within delegated access.
Specialized agents organize labs, medications, prior history, care gaps, follow-up actions, administrative flags, and quality checks for human review.
Agents read encounter notes, orders, payer policies, labs, imaging, therapy notes, medication history, and prior treatment history. They map supported, partially supported, and missing criteria, surface submission risk, draft medical necessity language, prepare AI-recommended code candidates for certified coder review, and assemble readiness checklists before packet generation.
Ask whether criteria are met, what evidence supports the case, what documentation is missing, and what action should be prepared next.
Agentic Workflow Pattern
The workflow runs domain subagents, saves structured outputs, evaluates answer quality, preserves citations and traces, and lets teams approve before acting.
Healthcare Product Demo
The healthcare workflow starts with a patient visit to the doctor’s clinic. DocIntel records or uploads the patient-clinician conversation, transcribes the visit, drafts clinical documentation, prepares patient-facing summaries and after-visit summaries, and uses RBAC so each persona works within the right permission boundary.
Open on YouTubeScribe Agentic AI Workflow
DocIntel keeps the provider or clinician in control while reducing manual documentation work. The provider/clinician can approve the workflow output. Clinicians, nurses, clinician assistants, or clinician admin teams can edit scribed information as needed before approval. Patients can view approved visit information and chat from saved data, while every persona can ask scoped questions against the information they are allowed to access. With the After Visit Summary agent, clinics can move from conversation capture to patient-ready instructions and saved artifacts in one workflow.
Capture a patient-doctor conversation with consent and select the spoken language for transcription.
Convert audio into a structured transcript, identify speakers, and normalize clinical details for review.
Identify symptoms, history, medications, vitals, allergies, assessment details, and relevant risk flags.
Generate SOAP note drafts, patient summaries, follow-up checklists, and administrative reminders.
Generate patient-facing AVS content with visit reason, clinician impression, tests, referrals, warnings, preventive reminders, and follow-up plan.
Clinicians, nurses, assistants, and admin teams can correct the draft within delegated access; the provider or clinician approves the final workflow packet.
Patients can view and chat from approved saved data; care teams can edit or prepare work based on role; all questions stay scoped to persona permissions.
Save the scribe packet beside labs, prior notes, referrals, payer guides, and other searchable records.
Use the structured packet for care-gap review, prior authorization evidence mapping, billing support, compliance traceability, and patient communication.
Structured Information Pulled By The Scribe
HealthCarePanel presents the scribe output as structured information with sources and confidence where available. This means the care team can review, edit, approve, search, and reuse the visit record across patient communication, clinical follow-up, prior authorization, billing support, and compliance review.
Patient name, date of birth, encounter date, provider, facility, and encounter type are organized as searchable visit context.
The patient-clinician conversation is preserved as source text so reviewers can trace generated outputs back to what was actually discussed.
Subjective, objective, assessment, and plan sections are drafted with medications discussed, orders or tests discussed, and clinician review notes.
The system prepares what was discussed, care-team recommendations, patient questions, and suggested questions for the next visit.
The AVS captures visit reason, clinician impression, today’s discussion, medication instructions, tests and orders, referrals, follow-up plan, warning signs, preventive reminders, facility coordination, and patient questions.
Follow-up actions, owners, due dates, priorities, sources, confidence, and open questions are extracted for care coordination.
Consent status, clinician-review requirement, PHI categories, redaction recommendations, and governance notes are captured for review.
After Visit Summary Demo
This demo shows how DocIntel coordinates specialized agents to turn a consent-based clinical conversation into structured notes, patient-friendly instructions, reviewable workflow output, and an After Visit Summary artifact that can be saved, embedded, and searched later through grounded chat.
Open on YouTubeHealthcarePanel Product Experience
HealthcarePanel now presents clinical, prior authorization, and scribe workflows as separate operational tabs. It loads the latest workflow run for a document, shows run status and agent steps, evaluates output quality, captures approval notes, supports persona-aware edits, records change history, and saves approved packets for later chat, retrieval, and downstream healthcare work.
Clinical workflow, prior authorization workflow, and clinical scribe workflow can be run or re-run independently based on the document or visit context.
Clinical scribe requires consent before recording or upload, supports browser recording, audio file upload, language selection, recording status, and clearing/replacing audio.
For a brand-new clinic visit, the panel can create a transcript document, embed it, save the scribe packet, and make the visit available for later chat.
Authorized personas can edit allowed sections such as SOAP notes, patient summaries, after-visit summaries, follow-ups, governance fields, and prior-auth packets.
Users can see workflow run ID, status, version, agent steps, errors, and whether the packet is pending approval or already approved.
The panel displays quality metrics, overall score, gate status, evaluator version, and recommendations so teams can review readiness before relying on output.
When a run is pending approval, authorized clinical users can add approval notes and save the approved packet for governed reuse.
Clinics can generate an AVS PDF, save it as a document, chunk and embed it, and make it available for later patient chat and retrieval.
Healthcare Workflow Demo
This demo shows how DocIntel supports the full prior authorization lifecycle. The workflow starts with encounter notes, orders, diagnosis context, payer policy, labs, imaging, therapy notes, medication history, or prior treatment history, then extracts the requested service, clinical indication, payer criteria, evidence found, missing information, submission risks, and a medical necessity draft for human review.
Open on YouTubeClinical and Prior Authorization Intelligence
DocIntel helps teams move from scattered healthcare documents to a reviewable prior authorization workflow. Clinical agents organize the patient story; prior authorization agents compare payer criteria against cited evidence; coding support recommends ICD-10-CM, CPT, HCPCS, RxNorm, or NDC candidates for certified reviewer approval; and operational tracking keeps draft, ready-to-submit, submitted, pending payer, approved, denied, appeal, peer-to-peer, and closed statuses visible.
Process encounter notes, service orders, diagnosis context, payer policy, labs, imaging, medication history, therapy notes, and prior treatment records.
Identify supported, partially supported, and missing criteria with citations, so teams can fix gaps before submission instead of after denial.
Create a missing information packet for review with focused requests for symptom duration, therapy history, medication trials, imaging findings, neurologic exam details, or other required evidence.
Recommend code candidates and require certified coder or qualified billing reviewer approval for final ICD-10-CM, CPT, HCPCS, RxNorm, or NDC rows.
Confirm requested service, reviewed codes, selected payer policy, mapped criteria, resolved or overridden missing evidence, medical necessity draft, and human review.
Create both a prior authorization packet PDF and a missing information packet for review, then manage case status, payer reference number, owner, submitted date, follow-up date, expected decision date, decision, and next action.
How DocIntel Simplifies the Workflow
Prior authorization is difficult because the work is split across providers, nurses, clinic admins, coders, billing teams, payer policies, patient records, and follow-up timelines. DocIntel brings the request packet, payer criteria, clinical evidence, missing information, coding readiness, generated review artifacts, approval gates, and case tracking into one reviewable workspace. It can generate both the prior authorization packet PDF and the missing information packet for review, so each persona can see what they need to do, what evidence supports the case, what is still missing, who must approve the next step, and where the case sits with the payer. The workflow stays controlled by healthcare professionals, while AI reduces the manual burden of finding evidence, checking policy requirements, drafting packet content, and keeping the case organized from request to decision.
Healthcare workflows support preparation, documentation review, and human decision-making. They do not replace clinical judgment, medical advice, provider review, or payer determination.
Restaurant and Fast Food Vertical
DocIntel now supports a restaurant owner/member intake workflow that records menu conversations, extracts restaurant profile and menu prices, preserves the transcript, and turns approved menu data into search, conversation, Stripe-paid carryout ordering, feedback, ratings, recommendations, and competitive pricing workflows.
DocIntel Restaurant Vertical Demo
This demo shows how a restaurant owner or authorized member records restaurant details and menu prices, reviews the extracted profile and menu rows, and stores approved data in the system. Food lovers can then use chat or speech to search menus, compare prices, add items to a carryout cart, pay through Stripe Checkout, and submit text or voice feedback after the experience.
Open on YouTubeDocIntel Restaurant Menu Scribe
The workflow records or uploads a restaurant owner/member conversation in one of five language choices: English, Spanish, Bangla, Hindi, or Arabic. DocIntel transcribes the audio, extracts restaurant details and menu rows, preserves the full source transcript, and requires human review before the profile and menu are saved for downstream search, conversations, recommendations, ordering, feedback, and price comparison.
Record or upload restaurant owner/member conversations, including long menu walkthroughs split into standalone audio segments.
Transcribes each segment, preserves owner/staff/customer speaker context where available, and merges a full reviewable transcript.
Extracts restaurant name, cuisine, description, address, phone, website, hours, service options, and payment options.
Finds categories, item names, prices, quantities, descriptions, dietary tags, spice level, options, and availability.
Normalizes price, currency, category, availability, and comparable menu fields without silently dropping long-menu rows.
Flags missing business fields, ambiguous prices, menu gaps, and owner-review notes before approval.
The owner or authorized operator edits and approves profile and menu rows before publishing or using them in comparisons.
Approved restaurants and menu items become searchable and comparable for conversation, Stripe-paid carryout ordering, feedback, recommendations, and competitive pricing.
Agentic AI Restaurant Recommender
The restaurant orchestrator reads the user’s question, conversation context, cuisine, location, budget, menu item, review quality, rating signals, verified feedback, and price intent. It then routes to the right specialist agent and returns a grounded answer with restaurant details, menu matches, prices, confidence, rating context, and freshness where available.
Open on YouTubeExtracts cuisine, location, meal type, dish, quantity, budget, party size, and dietary constraints from natural-language questions.
Finds restaurants by location, distance, cuisine, rating, review count, service type, and menu coverage.
Retrieves menus, sections, dishes, item availability, and hybrid menu matches from keyword and pgvector search.
Compares dish prices across restaurants, normalizes variants, filters irrelevant charges, and reports competitive price tables.
Analyzes customer text or voice feedback, suggests rating scores, maps topics, and tags signals for taste, value, portion, freshness, spice, packaging, wait time, and accuracy.
Combines menu match, price, rating, feedback volume, verified-order signals, sentiment, freshness, and constraints to produce ranked recommendations.
Customer Ordering and Feedback Loop
Recent DocIntel restaurant updates add a customer-facing paid ordering loop. A food lover can search by text or speech, add matched menu items from chat to a one-restaurant carryout cart, review customer details and pickup request, pay through Stripe Checkout, then leave typed or voice feedback. Stripe webhooks mark paid orders as submitted, return customers to the same workspace, and keep restaurant owners and staff connected to scoped order and feedback queues.
Open on YouTubeCustomers ask for dishes, cuisines, budgets, dietary needs, comparisons, or recommendations using chat or speech input.
Structured restaurant and menu IDs from approved menu data allow matching items to be added directly from the AI answer.
The customer reviews quantity, item notes, pickup time, phone, email, and order notes, then pays for the carryout-only order through Stripe Checkout.
Stripe webhook confirmation marks the order paid and submitted, then restaurant owners or staff can accept, reject, mark ready for pickup, or complete orders scoped to their matching restaurant email or workspace role.
Customers can submit restaurant or menu item feedback from a menu row or carryout order using typed text, recorded voice, or uploaded audio.
DocIntel analyzes sentiment, past rating context, restaurant/menu item context, and topic signals to suggest a 1-5 rating that the customer can override.
Feedback is categorized into tags such as taste, value, portion, freshness, spice, packaging, wait time, and accuracy.
Feedback linked to accessible carryout orders is marked verified and contributes to restaurant and menu-item rating badges.
If a paid order is rejected, DocIntel attempts a Stripe refund before marking it rejected; restaurant owners can also acknowledge, respond, resolve, or dismiss feedback in an operational queue.
Mobile Restaurant Food Ordering Experience
DocIntel is now optimized for both web and mobile experiences. A customer can be commuting, sitting at home, or away from a desktop and still use conversation to find menu items, compare options, add the right food to a carryout cart, pay through Stripe Checkout, and give rating or feedback after the order.
The same responsive React application works across desktop, laptop, tablet, and mobile browser without a separate app install. Desktop gives teams a full workspace for ingestion, RAG chat, summaries, traceability, billing, usage, and admin controls, while mobile keeps customer-facing conversation, ordering, feedback, and workflow actions accessible in the moment.
Open on YouTubeCustomers can open DocIntel from a mobile browser during commute, at home, between errands, or while deciding what to eat.
Ask by text or speech for dishes, cuisines, budgets, menu comparisons, recommendations, or availability from approved restaurant data.
Add matched menu items to a one-restaurant carryout cart, review pickup details, and complete payment through Stripe Checkout.
Payment return links bring customers back to the same workspace so they can continue reviewing order status and restaurant context.
After the order, customers can type or record feedback, and DocIntel can suggest ratings, sentiment, and tags such as taste, value, portion, wait time, and accuracy.
Teams keep the richer desktop workspace for operations, while customers and field users get fast self-service actions from mobile.
Operations Extension
The same agentic pattern can move from consumer food recommendations into staff workflows across inventory, POS, table context, location state, and operational analytics.
“Check if halibut is in stock, and if it is, add it to table 7’s order.”
Restaurant and Fast Food Workflow
The same browser-chat pattern can support restaurant discovery, menu owner intake, fast food comparisons, quick-service paid ordering, cloud kitchens, cafes, and staff operations. Customers can ask for food recommendations, pay for carryout orders from conversation, and leave feedback; operators can ask about menus, pricing, orders, payments, reviews, inventory, customer experience, location, waitlist, and analytics workflows.
Capture restaurant details, menu items, prices, service options, and source transcript from owner/member conversation.
Answer “best Thai dinner under $25,” “American fast food near Seattle,” or “seafood with good reviews” using grounded restaurant data.
Compare Pad Thai, Tom Yum, chicken tikka masala, appetizers, desserts, or other dishes by price, restaurant, distance, and rating.
Add matched menu items from conversation to cart, pay through Stripe Checkout, and let restaurant staff process paid order states.
Capture typed or voice feedback, suggest rating scores, tag topics, separate verified order feedback, and feed rating badges.
Extend the same agents into POS actions, inventory status, substitutions, waitlists, location routing, and daily analytics.
ADAR Sports Workflow
A central orchestrator routes text or voice questions in English, Bangla, Hindi, Arabic, Spanish, and Urdu to specialist sports agents that fetch and reason over current cricket information.
Scrapes league pages for current standings, schedules, recent results, upcoming matches, and umpiring assignments.
Finds latest match scorecards, reads dismissal details, compares innings, and summarizes what changed in a season.
Aggregates batting, bowling, schedule, match history, dismissals, win patterns, and team strength indicators.
Answers named-player questions, top performer queries, strike rates, wickets, season totals, and cross-team leaderboards.
Uses retrieval over league rulebooks and FAQs to answer umpiring, eligibility, wide-ball, no-ball, and format questions.
Scores responses for accuracy, completeness, relevance, and format so admins can monitor answer quality over time.
Sports AI Product Demo
This demo shows how the sports assistant coordinates league agents to answer cricket questions across live scorecards, schedules, teams, players, standings, rules, and season context. Users can ask natural-language questions by text or speech while the orchestrator chooses the right agent, fetches current data, reasons over stats, and returns a useful answer.
Voice and text conversation are supported in six languages: English, Bangla, Hindi, Arabic, Spanish, and Urdu. When a question is asked using speech, the assistant can respond back with voice as well as conversational text, making it easier to use during team discussions, match preparation, in-game rule lookups, and quick league research.
Open on YouTubeSports AI Subscription Demo
This demo shows how a cricket team can start using ADAR ARCL Assistant through a self-service registration path. A team user can create an account, enter team details, and move into secure subscription onboarding without waiting for a manual setup process.
The flow connects team registration, Stripe-powered billing, and the free trial experience so captains, managers, players, and league users can evaluate the assistant before monthly billing begins. After access is active, teams can use the assistant for scorecards, player stats, team strengths, schedules, standings, rules, polls, and multilingual text or voice conversations.
Open on YouTubeCricket League Assistant Pattern
The same Agentic AI pattern can support major leagues, minor leagues, community tournaments, school sports, fantasy communities, and other sports domains. Replace the cricket data sources and rules with the target league’s scorecards, roster data, schedules, standings, rulebooks, and historical stats.
The assistant accepts text or voice questions in English, Bangla, Hindi, Arabic, Spanish, or Urdu and detects whether the user is asking about scorecards, players, teams, rules, schedules, standings, or comparisons.
The orchestrator routes the request to live web scraping, scorecard, team stats, player stats, rules, or judge agents.
Agents fetch current league pages, recent matches, player records, team history, standings, and rule references.
The assistant explains player strengths, team performance, match context, season trends, and evidence behind the answer.
A judge agent can score accuracy, completeness, relevance, and format so league admins can monitor quality.
The framework can extend beyond ARCL cricket to other cricket leagues, baseball, soccer, basketball, football, tennis, and more.
Cultural and Social Video Intelligence
ADAR DocIntel Video Intelligence can help cultural, educational, family, community, and social videos become searchable knowledge instead of passive playback. Social and cultural moments can be processed into timestamped segments with visual context, transcript or narration signals where available, frame samples, captions, summaries, and chat-ready answers that help viewers understand what happened, where it happened, why it matters, and how the moment connects to a broader story.
Kathak Performance Example
A parent, teacher, student, performer, researcher, or community organizer can upload a Kathak performance video and ask natural questions such as “what is being performed here?”, “which section shows footwork?”, “summarize the performance between 2:00 and 5:00”, or “what cultural context is being explained?”
The same workflow can support dance recitals, music performances, cultural festivals, community events, training sessions, lectures, interviews, and social storytelling. DocIntel helps people move from watching a long video to finding moments, understanding sections, preserving context, and creating a searchable memory of the event.
Open Video Intelligence DemoCultural Video Intelligence Pattern
Video Intelligence can sample visual frames, preserve time-based structure, capture narration when available, summarize performance sections, and make cultural recordings available through grounded chat. The result is useful for learning, reflection, teaching, family memories, social sharing, community archives, and cultural preservation.
Identify important sections of a dance, music, narration, recital, lecture, or cultural event with timestamp-aware context.
Ask what happened in a specific time range, which moment shows footwork, what section is being explained, or what a viewer should notice.
Students, teachers, parents, performers, and researchers can summarize sections and revisit meaningful moments without scrubbing through the full video.
Festivals, school events, performances, interviews, and social recordings can become searchable archives for families and communities.
The same Video Intelligence foundation can support cultural performances, sports highlights, healthcare education, product demos, inspections, and enterprise recordings.
Answers can point back to time ranges, retrieved chunks, transcript signals, or visual context so the user can verify the moment.
Music and Cultural Knowledge
Agomonia Labs proudly presents ADAR Geetabitan, showing how Agentic Data Access and Reasoning can support Rabindra Sangeet discovery with Bengali conversation, song search, raag and taal metadata, notation, playback, evaluated answers, and community participation.
Music Product Demo
Inspired by Rabindranath Tagore, the first Asian Nobel Laureate and creator of more than two thousand songs, ADAR Geetabitan is a humble tribute to everyone who learns, performs, teaches, researches, or deeply loves Rabindra Sangeet.
Users can type or say a lyric line, emotion, mood, raga, theme, season, occasion, life stage, or personal reflection, and discover Rabindra Sangeet that resonates with what they are feeling. The assistant supports raag and taal exploration, notation review, song metadata, YouTube playback, and Bengali voice conversation.
Geetabitan is also a community-centered product for learners, performers, teachers, researchers, and admirers of Tagore's creations. Users can help improve the knowledge base by sharing insights, correcting lyrics or metadata, adding notation and translations, contributing historical or musical context, and suggesting product improvements.
Open on YouTubeGeetabitan Assistant Pattern
The Geetabitan assistant is not a generic chatbot. It is a specialized ADAR vertical shaped around Rabindra Sangeet workflows, Bengali language interaction, curated music metadata, community contribution, and self-service subscription access.
Search Rabindra Sangeet by title, lyric line, emotion, mood, season, occasion, life stage, or conversational Bengali queries.
Explore musical structure with raag, taal, style, and contextual metadata for selected songs.
Display notation-style views and musical patterns so learners can inspect how a song is structured.
Ask questions in Bengali voice, continue follow-ups, and stop spoken output with natural commands.
Support corrections, metadata improvements, notation, translations, historical context, musical context, and feature feedback.
Use YouTube playback inside the app experience and subscribe through self-service access as the product grows.
Real Estate Lease Intelligence
DocIntel applies vertical Agentic AI workflows on top of its conversational RAG foundation to turn commercial or residential lease documents into structured, searchable, and action-ready intelligence. Lease-specific agents extract parties, property context, rent terms, deposits, renewals, notice windows, maintenance responsibilities, restrictions, compliance language, default clauses, and risk signals. The same approved lease context can then support property managers, landlords, tenants, brokers, legal teams, finance teams, and operations teams with cited answers, obligation tracking, summaries, comparisons, and follow-up workflows.
Lease Intelligence Product Demo
This demo shows how a lease document is processed by a domain-specific workflow. The system extracts key lease information, saves structured outputs, and lets users ask conversational questions with answers grounded in the lease text and citations.
Open on YouTubeVertical Agentic AI Workflow
The lease workflow keeps DocIntel's conversational RAG foundation in place, then adds lease-specific agents for extraction, obligation mapping, risk review, financial term analysis, date tracking, citation lookup, and workflow-ready outputs. Each persona can ask questions in plain language, review cited evidence, and act on the lease details that matter to their role.
Upload the lease, OCR scanned pages, classify the document, preserve source context, and prepare retrievable chunks for cited answers.
Extract landlord, tenant, property address, premises description, lease type, effective date, execution details, and governing context.
Pull base rent, deposits, escalations, late fees, CAM charges, utilities, taxes, insurance, reimbursements, and payment schedules for finance review.
Capture commencement, expiration, renewal windows, notice periods, maintenance duties, repair obligations, inspection rights, and delivery requirements.
Surface assignment, subletting, termination, default, indemnity, exclusivity, permitted use, compliance, unusual language, and missing or ambiguous terms.
Ask plain-language lease questions, verify cited clauses, generate summaries, create follow-up checklists, and hand off action items to the right persona.
Finance and Tax Vertical
Most financial workflows do not fail because information is missing. They fail because information is scattered across W-2s, 1099s, 1098 mortgage statements, prior-year tax returns, brokerage statements, retirement records, mortgage documents, property tax records, bank statements, credit card statements, PDFs, scans, images, and client intake notes. Finance and Tax Planning brings these documents into one structured workflow so teams can move from uploaded files to organized, review-ready financial intelligence for tax preparation, financial planning, advisor review, and operational follow-up.
Financial Documents
DocIntel Intelligence Layer
Readiness Views
Finance and Tax Planning Product Demo
This demo shows how a taxpayer, preparer, advisor, or operations reviewer can select chunked or embedded financial documents, provide client context, tax year, filing status, planning notes, and reviewer notes, then run a guided readiness workflow. DocIntel prepares organized packets so professionals start from a review-ready position instead of a pile of scattered documents.
The workflow produces an overview, document list, tax organizer, missing-items checklist, prior-year comparison, financial planning snapshot, advisor questions, and a CPA/EA or advisor review packet. After review, DocIntel can create an advisor packet PDF and upload the approved artifact back into the workspace so it can be viewed, downloaded, searched, reused in chat, and traced through audit history and notifications.
Open on YouTubeVertical Agentic AI Workflow
The finance workflow keeps DocIntel's horizontal foundation in place: document ingestion, OCR and image-aware extraction, classification, semantic chunking, embeddings, structured workflow agents, review states, approval status, audit logging, and reusable vertical UI panels. Finance-specific agents, deterministic parsers, and regular expressions then extract tax values, account balances, mortgage principal, property values, retirement ending balances, brokerage activity, bank balances, credit card balances, cash-flow signals, planning gaps, and review flags from imperfect PDFs, scans, images, and provider-specific formats.
Select W-2s, 1099s, 1098s, brokerage statements, retirement statements, charitable receipts, prior-year returns, scans, images, and client notes from the DocIntel document library.
Identify document types and route each file to the right extraction path for tax forms, income statements, mortgage documents, property records, retirement accounts, bank statements, credit cards, brokerage activity, and supporting notes.
Pull key labels, values, box numbers, amounts, withholding, mortgage interest, property tax, income, deduction, credit, cost basis, contribution details, balances, liabilities, cash-flow signals, and account values.
Organize detected forms, income summary, deduction and credit summary, document summaries, and review flags into a structured workspace.
Create a planning view across net worth, cash flow, assets, liabilities, retirement signals, brokerage activity, mortgage principal, property values, bank balances, credit card activity, and advisor notes.
Identify missing tax or planning documents, unanswered intake questions, incomplete financial signals, and advisor follow-up items before the review packet is finalized.
Use prior-year return signals and current financial documents as a baseline for reviewer checks across income, withholding, deductions, carryforwards, account balances, debt, and year-over-year planning changes.
Generate a CPA/EA or advisor review packet and create an advisor packet PDF with summary, readiness status, planning signals, next actions, source evidence, guardrails, and reviewer notes.
Route the packet through human approval so the reviewer can inspect, edit, save reviewed sections, approve, reject, upload the approved PDF packet back into the workspace, withdraw, download, and audit the outcome.
DocIntel does not replace tax professionals, financial advisors, or filing authority. AI-assisted extraction, readiness scoring, and planning summaries are drafts that must be reviewed by a qualified CPA, EA, certified reviewer, financial advisor, or tax professional before tax submission, planning, or advisory decisions are made.