AntHill is not a chat interface with a database attached. It is three distinct infrastructure layers — the Context Graph, the Ontology Layer, and the Agent Architecture — designed to sit between your organization's knowledge and any AI model, and to compound in value with every question they help answer.
Every question routes through the same four stages: context retrieval from the graph, grounding against the ontology, agent execution, and decision trace writeback. Human review at every meaningful checkpoint. No stage is a black box.
A bi-temporal, permissioned knowledge graph of every decision, metric definition, incident, and tested hypothesis your organization has ever recorded. This is the substrate. Everything else is built on top.
Slack, Confluence, JIRA, and Git cover where real organizational knowledge actually lives. Every other integration is noise until these four are load-bearing. We will expand later. We will not fragment the graph with thin, high-volume sources before the core is trusted.
Every edge in the graph carries two timestamps: when it was created in reality, and when AntHill learned about it. That means a question about card activation three months ago gets answered against the metric definition that was in force three months ago — not the one in force today.
Decisions, hypotheses, incidents, metrics, people, tickets, PRs, deploys, postmortems, runbooks, schemas, on-call logs, meeting notes, RFCs. Each type has its own extraction pipeline tuned for its format. No single generic "chunk everything" strategy.
Every node in the graph carries the same access controls as its source system. An analyst querying the graph sees exactly what they would see in Slack, Confluence, JIRA, or Git — no more. This is not a compliance afterthought. It is a design constraint on every retrieval.
A grounded map of your organization's metrics, schemas, business rules, and trusted query patterns. Eliminates the class of hallucination that destroys trust in text-to-SQL. Auto-populated from your existing SQL history, then tuned with your analysts.
Every metric in the ontology is grounded in the exact tables, filters, and edge cases your analytics team agreed on — captured from the SQL they've already written. When a PM asks about GTV, the model reasons over your definition. Not an industry average.
The ontology carries the semantic meaning of every table and column — not just its data type. That means the SQL agent knows why it's selecting a join, not just that the join compiles. Queries land right the first time.
Analysts promote queries they trust into a curated library. The ontology uses them as reference patterns. New questions compose from trusted parts, not from scratch. The library grows. The compound gets faster and more reliable.
"Exclude internal test accounts." "Filter out the migration cohort from January." "Weight the APAC region by exchange rate as of close." Rules like these live in the ontology, not in tribal memory. Every answer respects them, automatically.
Six specialized agents, orchestrated. Each one does one thing, with human review at every meaningful checkpoint. Nothing acts autonomously on consequential decisions. Everything shows its work.
Breaks a natural-language question into structured, ranked hypotheses. Uses indexed context to weight plausibility. A "why did X happen" question that would generate 20 human-produced hypotheses typically yields 10–15 ranked ones here.
For each hypothesis, retrieves the Context Graph subgraph most likely to contain validation or refutation. Bi-temporally aware. Permissioned. Cited at the node level.
Generates SQL grounded in the Ontology Layer, executes it against the warehouse, validates the result shape. Catches class-of-hallucination errors before they reach inference.
Synthesizes retrieved context and query results into a hypothesis verdict — validated, refuted, or insufficient. "Insufficient" is a first-class outcome. The agent never invents plausibility to fill a gap.
Before any output reaches the analyst, a validation pass checks citation completeness, confidence calibration, and consistency with prior decisions. The failure mode of "confident but wrong" is the one AntHill is architected to eliminate.
Every resolved question writes a structured decision trace back to Confluence — cited, permissioned, queryable. Next time someone asks the same question, the answer arrives from memory, not from scratch.
-- Context OS · Decision Intelligence -- Query: "What is causing Credit Card MTU to drop 10% MoM?" -- Context: Jira · Confluence · Slack · DataWarehouse · 847 nodes activated HYPOTHESIS 1 of 12 TYPE Onboarding friction spike CONFIDENCE 87% EVIDENCE KYC step-3 drop-off increased +34% post PROD-2847 SOURCE Jira · PROD-2847 · merged 14 Mar 2026 IMPACT ~92,000 affected users · $2.1M GTV exposure ACTION Revert KYC flow to v2.1 or patch step 3 validation RESOLVED 14 minutes · human baseline: 2–3 days DECISION Auto-written to Confluence/Decisions/Q1-2026-KYC-Incident
These are not hypothetical. Each one is live today at our first deployment — and each one is a concrete entry point for a new design partner.
The highest-frequency, highest-cost workflow in analytics. A metric drops. Twenty hypotheses arrive. Archaeology begins. Five days later, an answer. With AntHill: the platform decomposes, retrieves context, executes SQL, validates, and documents — in parallel, with human review at each step.
Payment reconciliation across issuer, payment provider, receiver, merchant. Manual today. Non-negotiable every day. AntHill retrieves the schema, identifies correct tables, generates reconciliation logic, executes it, and surfaces discrepancies with source citations. The analyst confirms.
Constraints arrive: shift sizes, break structures, gender proportion, language splits, productivity targets. Translating constraints into code is manual today. AntHill interprets the optimization problem, generates the logic, runs iterations autonomously, returns a validated output satisfying all KPIs. The analyst reviews.
The Head of Product spots something odd and, instead of messaging an analyst and waiting 24–48 hours, asks AntHill directly. Historical patterns, prior investigations, calendar overlays, metric definition history — queried against the graph. A cited answer arrives in minutes. The product decision moves at thinking speed.
AntHill's onboarding is a forward-deployed model. A dedicated engineer partners with your team through security review, integrations, and ontology tuning. First value lands in the first week of live usage — the compounding starts there and never stops.
Security review, data governance alignment, permissions mapping, DPA execution. The prerequisite for trust.
Slack, Confluence, JIRA, and Git connected. Warehouse credentials scoped. Context Graph begins ingestion.
Metric definitions captured from existing SQL. Business rules encoded. Trusted query library seeded by analysts.
A real diagnostic question, answered in production, with citations. The pilot success condition. The pattern begins.
We know what's tempting to build. We are refusing most of it, on purpose. Focus is how infrastructure products become load-bearing. Surface area is how they become demoware.
A distribution feature, not a core value feature. We will build it when ten customers are paying. Before then, it's a demo trick that distracts from the infrastructure that actually matters.
Automated dashboard and visualization generation for long-horizon exploration. Twelve-month roadmap item. The diagnostic cycle is the proof point; dashboard generation is the expansion.
Full SOC2 certification and formal SLA commitments arrive after the first three design partners. MVP is designed for a partner tolerating controlled rough edges — not a cold RFP process.
Slack, Confluence, JIRA, and Git cover 80–90% of organizational knowledge. Everything else is noise at this stage. The graph gets broader when the core is load-bearing. Not before.
Design partner slots are limited. The earlier you start, the longer your context graph has compounded by the time the category lands.