Context OS · Enterprise Analytics Infrastructure

The context
layer that
makes AI
actually work.

A simple business question takes 2–4 hours to answer. Not because analysts are slow. Because 70% of their time goes to context retrieval. AntHill solves this at the infrastructure level.

Request design partner access See the platform →
$138B
Analytics labor pain annually
70%
Analyst time — context retrieval
25%
Bandwidth recovered · in production
12–15
FTEs freed · without headcount
AntHill · Query agent
→ "What is GTV by city for last 30 days, only completed orders?"
-- ontology v0.4.2 · bi-temporal: on SELECT city_name, SUM(order_value) AS gtv, COUNT(order_id) AS orders FROM orders_fact WHERE order_date BETWEEN CURRENT_DATE - INTERVAL '30 days' AND CURRENT_DATE AND status = 'completed' GROUP BY city_name ORDER BY gtv DESC;
Result · 3 rows · 0.4s
Bangalore₹4.2Cr
Mumbai₹3.8Cr
Delhi NCR₹2.9Cr
The problem
Context is the
missing layer.
$63K
Lost per analyst, per year
Entirely from context retrieval overhead — finding metric definitions, understanding business rules, hunting for prior decisions.
6 mo.
To build internally
5–6 FTEs for 6 months. That’s the cost for one team to build what AntHill delivers on day 1. Most still fail.
0
Standard for context graph efficacy
No unified standard exists. Vendors sell AI. No one owns the knowledge layer underneath. That’s the gap AntHill occupies.
“Your data stack works. Your AI models work. But the layer connecting your business vocabulary to your data — that doesn’t exist yet. AntHill is that layer.”
The platform
Three pillars.
One context graph.

AntHill is infrastructure — not a tool you try. It sits between your data stack and your teams, making every AI interaction accurate from day one.

Pillar 01
Platform Orchestration
Connects Slack, Confluence, JIRA, Git, Trino, and your SQL history into a unified signal layer. Deployment in days, not months.
Pillar 02
Business Context Graph
A living, bi-temporal knowledge graph of every metric definition, business rule, and decision in your organization. Compounds with every use.
Pillar 03
BFSI Ontology Layer
Pre-built domain ontology covering collections, lending, payments, risk, and compliance vocabularies. RBI-aligned. India-first.
Platform Orchestration · Active Live
Connected in days.
Not months.
AntHill ingests your full data environment — Slack threads, Confluence docs, JIRA tickets, Git commit messages, and your existing SQL history. Every artifact becomes context. The graph starts learning from day one.
SlackConfluenceJIRAGitTrinoBigQuerySnowflake
2–5 days
Time to first context
0 FTE
Integration overhead
84,291 nodes.
98.2% accuracy.
Bi-temporal context graph — every node carries when it was true, and when we learned it. Metric definitions never drift. Business rules stay enforced. The graph compounds: by month 6, a competitor starting fresh starts at zero.
Bi-temporalKnowledge GraphMetric RegistryBusiness Rules
84K
Ontology nodes
98.2%
Accuracy · live
BFSI vocabulary.
Already mapped.
Pre-built ontology covering collections, lending, payments, risk, and regulatory compliance for India FinTech. RBI frameworks mapped. EU AI Act Article 10 data lineage built in — ready for the August 2026 deadline.
CollectionsLendingPaymentsRBIEU AI Act Art. 10
Aug 2026
EU AI Act deadline
RBI
Compliance ready
How it works
From deployment
to decisions in days.
Step 01
Connect your stack
Slack, Confluence, Git, Trino. AntHill ingests your existing environment. No rip-and-replace.
Step 02
Context graph builds
AntHill auto-populates the ontology from SQL history, docs, and tickets. 80%+ auto-mapped from day one.
Step 03
Teams ask in Slack
Business questions in natural language. AntHill generates SQL, validates against ontology, returns verified answers.
Step 04
Graph compounds
Every interaction enriches the context graph. A competitor starting fresh at month 6 starts at zero.
Traction
43 active conversations.
India-first.
43+
Pipeline leads · Active
Design partner conversations open across India’s leading consumer tech, fintech, and quick commerce companies. US expansion: 12–15 months.
BFSI / FinTech
Axis Bank · InCred · Razorpay · Digit Insurance
14 leads
Quick Commerce
Swiggy · Zepto · Blinkit
8 leads
Media & Consumer
JioHotstar · PocketFM · InMobi
9 leads
Enterprise SaaS
Freshworks · Myntra · Meesho · Cred
12 leads
Why AntHill
Not a tool.
Infrastructure.
CapabilityGlean / HexInternal buildAntHill
BFSI domain ontology Generic only6 months to buildPre-built · day 1
Data analysis agents 12–18 months awayUnresolvedLive today
Bi-temporal context graph NoneNoneCore architecture
EU AI Act Art. 10 readiness ManualManualBuilt in · Aug 2026
Slack-first deployment NoNoCore channel
Compounds over time No — static indexRequires reworkGraph learns continuously
Time to first value Weeks–months6+ months2–5 days
Pricing
Aligned to value.
Not seat count.
Starter
Context OS — Starter
₹15–25L/year
Up to 50 users · 3 data sources
Platform orchestration layer
Context graph — up to 10K nodes
Slack-first deployment
Standard BFSI ontology
Email support
Growth · Most common
Context OS — Growth
₹40–75L/year
Up to 200 users · unlimited data sources
Full platform orchestration
Unlimited context graph nodes
Custom ontology extensions
Deep agent architecture
EU AI Act Article 10 data lineage
Dedicated CSM
Enterprise
Context OS — Enterprise
₹75–150L/year
Unlimited users · on-prem or VPC
All Growth features
On-premise or private VPC deployment
Custom BFSI ontology build
SLA-backed 99.9% uptime
RBI compliance support
Co-develop roadmap
Design partner program
Work with us
before we launch.

We’re selecting 5–8 design partners across BFSI and quick commerce. Co-build the ontology. Shape the product. Lock founding-customer pricing.

India-first · Bangalore · April 2026