One engine. Every agent.
NL2SQL, Document AI and Agent Studio are the shared core. Enterprise Intelligence (invoice, contract, spend, compliance) and Healthcare Intelligence (risk adjustment, RADV, clinical evidence) are built on top — cross-checking documents against your data and automating work end-to-end, all in your own cloud.
One pipeline. Four agent outputs. Zero data movement.
Your Data Sources
Healthcare
Read-only · Zero egress
InferonIQ AI Engine
What You Get
Five steps, from raw data to recovered cash.
The walkthrough plays automatically — or jump to any step to see how the agents process your data end to end.
Connect any source
Pull invoices, contracts, databases and clinical records from any system. Read-only connections — your data never moves.
One platform. Six working surfaces.
Every module runs on the same catalog, the same governance and the same agents — so what you connect once is understood everywhere.
Turn clinical evidence into defensible action.
Connect claims, encounters, clinical documents and member data. InferonIQ finds the supporting evidence, validates HCCs, surfaces suspected missing conditions and opens a traceable review for Risk Adjustment, RADV and coding teams.
Captured from the live product on synthetic members and charts. No real patient data (PHI) is shown.
HCC Validation
Reconcile every reported diagnosis against encounters and the chart, map ICD-10 to CMS-HCC V28 for the payment year, apply CMS hierarchies and put a RAF and annual dollar value on every finding.
- ICD-10 → CMS-HCC V28 by payment year, hierarchies applied
- RAF and annual $ impact per finding, from official CMS tables
- Supported, unsupported, historical or conflicting
MEAT Evidence
Look for proof the provider monitored, evaluated, assessed or treated the condition — not just that it was listed. A problem-list or history-only mention is not accepted as support.
- Assessment & plan, labs, medications and imaging cited
- Quoted text with document and page
- Current-year date-of-service checks
Suspect Conditions
Surface diagnoses the chart documents — or pharmacy fills and lab results imply — that no claim carries, value each one in RAF and dollars, and send it to a coder, a chart chase or a provider query. Nothing is auto-coded.
- Documented but not submitted, or at the wrong specificity
- Pharmacy and lab signals with no claim (apixaban, eGFR, A1c)
- Opportunity valued net of V28 hierarchies
Documentation Gaps
Turn unsupported and chart-less diagnoses — and conditions only pharmacy or lab data shows — into a prioritized chart-chase worklist, send non-leading provider queries where the note falls short, and track every request until the returned chart has been reviewed.
- One request per chart, ranked by HCC and RADV risk, dollar value shown
- CSV export and status re-import for retrieval vendors
- Returned charts matched and re-reviewed automatically
RADV Readiness
Trace every finding back to member, encounter, document, page and evidence before an auditor asks for it.
- Member → encounter → page provenance
- Readiness score across open findings
- Evidence export with reviewer decisions
Evidence Assurance
Prove the program itself is right: the record is checked, the RAF arithmetic is checked against CMS, a second reviewer samples decisions and every dollar is tracked from identified to realized.
- Signature, credential and record-type checks, citing CMS guidance
- RAF arithmetic equals the CMS V28 scorer on 954 test members
- Second-level QA and a value ledger, each member / HCC / year counted once
Reviewer Guidance & History
Every finding explains itself: what Agree and Disagree mean, how to review it, the RAF calculation step by step, and a history of what changed it and why.
- “How to review this finding”, written for the coder
- Step-by-step RAF and estimated dollar calculation
- Change history with the cause: rules, CMS / ICD release, base rate, segment or a new chart
Healthcare Ask AI
Ask questions across claims, encounters, labs, medications and clinical documents in plain English.
- Claims + chart in one question
- Governed, PHI-aware answers
- Every number traced to its source
From evidence to action
Same NL2SQL, Document AI, workflow and audit engine as Enterprise Intelligence.
- 1ConnectClaims · Encounters · Labs · Medications · Clinical PDFs
- 2UnderstandDocument AI · Clinical concepts · ICD-10 · Member timeline
- 3ReconcileClaims ↔ Encounter ↔ Clinical evidence
- 4EvaluateHCC rules · MEAT · Current-year evidence · Conflicts
- 5FindingsClassified, evidence-linked, ranked by impactSupportedUnsupportedMissing candidateHistoricalConflictNeeds review
- 6ActCoder review → Record request → Provider query → Audit evidence
Two agents. One cross-check.
The Invoice Agent reads what you're being charged. The Contract Agent knows what you agreed to. Together, they surface money you'd never find manually.
Freight Leakage
Missed Rebate Dollars
Supplier SLA Breach
Expired Agreement Spend
From receipt to ERP posting. Zero manual steps.
Complete invoice lifecycle — AI handles extraction, matching, approval routing, vendor disputes, and ERP posting. No templates. No rules to write.
Straight-through processing tracks % of invoices with zero human touch. Self-learning rules adapt per vendor automatically.
Never miss a renewal or escalation clause.
AI extracts every auto-renewal date, price escalation, liability cap, and obligation from your contract portfolio. Risk-scored and monitored 24/7 with proactive alerts.
Multi-Agent Architecture
Invoice Agent catches overcharges. Contract Agent knows what you agreed to.
Healthcare Agent backs every HCC with evidence. Query Agent answers anything you ask.
Together, they find money no single tool can. Describe any workflow in English — get a live operational app in minutes.
Describe a workflow. Get a live app. In plain English.
Not just dashboards — operational workflows with approvals, routing, alerts, and SLAs. Describe what you need in natural language. InferonIQ builds, connects, and deploys a working app in minutes. No code. No tickets.
Example prompt:
“Build an invoice exception review app where AP analysts approve or reject mismatches above $5,000. Auto-route rejected invoices to procurement and send Slack alerts.”
Ask in plain English.
Get instant answers.
From question to answer in seconds — across databases, documents, invoices, and contracts. No semantic models to build. No SQL to write. InferonIQ reasons across both structured and unstructured enterprise data.
Ask in plain English. Get instant answers.
“Which vendors overcharged us this quarter?” — InferonIQ reasons across structured databases and unstructured documents alike. Invoices, contracts, emails — all queryable in plain English with full lineage.
Invoice INV-2026-0142 from Meridian Supply Co. (3 line items, $18,500) matched against PO-4417. Line items: 1 matched, 1 not found, 1 freight/service. Total leakage: $7,650.00. Requires review before payment approval.
| Description | Qty | Unit | Invoiced | Expected | Status |
|---|---|---|---|---|---|
| Assembly Component, Grade A | 400 | $27.63 | $11,050.00 | $10,400.00 | MATCHED |
| Packaging Unit (case of 20) | 300 | $22.00 | $6,600.00 | — | NOT FOUND |
| Freight / Delivery | 1 | $850.00 | $850.00 | — | FREIGHT/SERVICE |
Zero semantic modeling. Instant understanding.
Connect a database and InferonIQ automatically catalogs every table, profiles columns, infers foreign keys, and generates AI descriptions — in under 45 seconds for 50 tables. No dbt. No semantic layer. No manual mapping.
Gets smarter with every interaction.
Every query, correction, and match decision feeds back into your isolated AI model. 8 self-improving mechanisms work continuously — no other customer sees your data or benefits from your patterns. Your instance is yours alone.
Repeat queries return in 50ms
Same mistake never repeated
Understands your business language
Good patterns rise, weak ones fall
Adapts to your risk tolerance
Vendor SKU → ERP SKU mapping
Auto-catalogs + AI descriptions
Admin-curated governance override
All mechanisms are securely isolated by customer_id. No cross-tenant data leakage. Your AI model is yours alone.
Cross-platform. Production-ready.
One question across Snowflake + Oracle + SQL Server. Each source runs its own dialect-native SQL in parallel, joins are measured against the data before they are trusted, and a self-healing pipeline recovers from errors without human intervention.
Thousands of tables in. A handful reach the model.
Generating SQL is the easy part. The hard part is knowing what your data means. InferonIQ builds a semantic map of your estate (meaning, measured relationships, your vocabulary) and uses it to send each question to the few tables and documents that can answer it.
- 01 · Your data estate10,000 tables
Warehouses, ERPs and document stores, with cryptic names, thin documentation and missing keys.
- 02 · Semantic catalogBuilt once
Each source is profiled within fixed bounds, described from its own samples, related by measured overlap and indexed by business meaning.
- 03 · Routing~100 ms
The question is matched to the catalog by meaning, not by table name.
- 04 · Working set≤ 20 tables
The model sees only the relevant tables, the most relevant columns of each, trusted joins and your glossary.
- 05 · ExecutionIn place
Dialect-native SQL runs inside each source. Only filtered or aggregated results come back.
- 06 · AnswerReconciled
Results from different systems are joined on measured keys, with the SQL and sources shown.
Dynamic
No semantic model to build before the first question.
- AI drafts table and column descriptions, synonyms and joins from the schema and real samples
- Your edits are never overwritten and change what the engine finds straight away
- Re-profiling only touches tables that changed; documented columns skip the model
- Exceptions are yours to tune: switch any check off, change its severity, or add your own rules
Scalable
Cost and prompt size stay flat as the estate grows.
- Tables above a million rows are profiled from a bounded sample, not a full scan
- Descriptions are written in batches of about 15 tables per model call
- Every question works from at most 20 tables across all sources, however many you connect
- Filters and aggregations run inside your warehouse; no bulk copies, no ETL
Built for messy data
Measures what it infers instead of trusting names.
- Inferred joins are checked against the data (value overlap and key uniqueness) before they are trusted
- Cryptic names are found by meaning: WRK_REQ answers a question about repair tickets
- Questions span databases and documents: contract clauses and obligations are joined with ERP rows
- If a source hits a row limit, the answer says the result may be incomplete instead of guessing
Fast
Measured on our routing benchmark and live connections.
- Median routing time ~105 ms; right tables picked on 70 of 70 benchmark questions and 16 of 16 held-out ones
- One question embedding is reused across routing, retrieval and memory
- Stable schema context is prompt-cached; repeat questions come from query memory
- SQL is generated natively for each source’s dialect: SQL Server, Oracle, MySQL, PostgreSQL, Snowflake, BigQuery and more
Everything the engine gives you.
Proactive Next Best Actions
Every answer suggests specific actions: Approve, Monitor, Investigate, Automate, Prevent, Escalate. Context-aware reasoning across Ask → Understand → Detect → Recommend → Act → Automate.
Live NL2SQL on YOUR Data
Query your actual databases in plain English. "Show top 10 vendors by spend this quarter" → accurate answer in 1.5-3 seconds.
Agent Studio — No-Code Apps
Describe a workflow in plain English. Get a live operational app with approvals, routing, SLA timers, and alerts. Not a dashboard — a working system.
Federated Cross-System Intelligence
One question across Oracle + SQL Server + Snowflake. No bulk replication: each source filters and aggregates in place, then results are joined on measured keys.
BYOC / On-Prem Deployment
Sealed container inside your VPC. Your data never leaves. Only AI requests exit — the question, the relevant schema and the result being summarised — to the provider you approve.
Connects to anything
Native connectors for Oracle, SAP, SQL Server, Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, MySQL, SharePoint, S3, Google Drive and email — anything else over JDBC or REST API.
Gain-Share Pricing
We earn a share of recovered cash. Find nothing? Pay nothing. No per-page fees. Self-funding from your own waste.
ERP Extract Generation
Matched invoices exported as ERP-ready posting files — Oracle, SAP, NetSuite, SQL Server. Zero re-keying.
What inferonIQ replaces.
The line items this platform removes from your budget — and how long each one used to take.
| Traditional Approach | Cost | Time | inferonIQ |
|---|---|---|---|
| Data catalog project (Alation, Collibra) | $300K–$1M/yr | 6–12 months | Automatic — 45 seconds |
| Schema documentation sprint | $50K–$150K | 2–4 months | AI-inferred at connection time |
| Custom BI dashboards per question | $5K–$20K each | 1–4 weeks | Natural language — instant |
| Data analyst headcount (per question) | $120K–$180K/yr | Days per ticket | Self-service — seconds |
| ETL for cross-system reporting | $200K–$500K | 3–6 months | Federated query — zero ETL |
Your data never leaves your cloud.
BYOC deploys as a sealed container inside your infrastructure. Read-only connections, a full audit trail and enterprise governance from day one.
Messy data, large estates: common questions
What data and architecture teams ask before connecting a source
Our metadata is poor: cryptic table names, few comments, no foreign keys. Will it still work?
We have thousands of tables. Do you send the whole schema to the AI?
How do you find joins when our database has no declared keys?
Do you copy our databases?
How do questions across several databases work?
What does onboarding a large estate cost in AI calls?
Can our team correct what the AI inferred?
Can we configure which exceptions are flagged?
Have more questions? Contact us