UBER
4 months
blew through our AI budget in a quarter, for the whole year essentially
Uber CEO
Agentic coding spend now capped per engineer per tool
Why Adapts
Understanding at enterprise scale.
Continuous context. Critical logic made clear. Agents grounded in your estate.
Results
Measured impact at enterprise scale
18 months → 1 month
Core banking modernization
A large regulated bank used Adapts to accelerate their core banking modernization. Adapts HC3 mapped 12 million lines of legacy COBOL, identifying critical business logic that manual discovery had missed for over a year.
a large regulated bank
3 weeks → 2 days
New architect onboarding, fully productive
An enterprise engineering team used Adapts to onboard new architects to a complex microservices platform. Instead of three weeks of tribal knowledge transfer, architects reached full productivity in two days with grounded docs and dependency maps from Adapts HC3.
Enterprise SaaS Platform
Months → weeks
System behavior discovery for compliance audit
A regulated energy company needed to document every system interaction for a compliance audit. Adapts mapped system behaviors that would have taken months to trace manually.
Regulated Energy Company
$2.4M saved
Parallel system costs eliminated during migration
An insurance company was running legacy and modern systems in parallel at $200K/month. Adapts accelerated the migration, eliminating twelve months of parallel operation.
a regulated insurance enterprise
Trusted by enterprise teams
The cost factor
AI budgets are breaking early
Agentic tasks burn tokens on rediscovery. Grounded on the Adapts map, agents use 80% fewer tokens in production.
WIPRO
5 to 10x
AI without the right process orchestration is a very expensive experiment.
Wipro Global CIO
Cloud bills vs forecast
THE PATTERN
$500M
my company spent my entire 2026 budget in Q1
Enterprises to Sam Altman
Gartner: AI agent software ~$207B in 2026, up more than 139% YoY
Sources: Fortune and TechCrunch (Uber, 2026); CIO Dive (Wipro, 2025); Inc. and KPMG (2026); Gartner via Fortune (2026).
The scaling factor
Agents hit a wall. A map does not.
Beyond a few hundred repositories, agents sample and guess. A map query costs the same at 5 repos or 10,000.
1 to 5 repositories · Agents thrive
The map adds little here, so we switch it off.
10 to a few hundred · Cost climbs
Lookup chains cross services; every task pays more to find its footing.
Beyond a few hundred · Coverage collapses
No context window holds the estate, so the agent samples and guesses.
AGENTS EXPLORING, NO MAP
$80 per task · ≈ $5M a year, for sampled answers
READING THE ADAPTS HC3 MAP
$2 per task · ≈ $0.5M a year, answers indexed and current
Per-task figures are the chart endpoints; annual figures scale the cost model to 200 developers. Illustrative.
The languages factor
One map spans the mainframe and the microservice
70+ languages, legacy to modern, plus the config that binds them. One impact analysis can trace a COBOL copybook to the API that consumes it.
70+ PROGRAMMING LANGUAGES
COBOL · CICS · RPG · Lotus Notes · Java · .NET · C/C++ · Python · Go · Rust · SQL · GraphQL · SPARQL · Terraform · YAML · XML
The standardization factor
One deterministic answer, organization-wide
The map is built from the code and re-indexed on every commit, so the same question returns the same answer for architects, developers, agents, program managers, and analysts.
without a map: five versions of the truth
one deterministic answer, organization-wide
Grounded enterprise context
The layer agents stand on
Adapts HC3 is a living code ontology that grounds agents instead of replacing them. Runs where your code runs, with no data egress.
Claude CodeCursorCodexCopilot
ADAPTS HC3 · deterministic code ontology, re-indexed on every commit
repospackagesdependenciesdeployable unitsworkflows
Runs where your code runs: AWS · Azure · GCP · on-premises · air-gapped / sovereign cloud · no data egress
Proven on real enterprise code
80%
fewer agent tokens
1B+
lines of code mapped
70+
languages understood
70%
faster discovery
Modernization discovery compressed from 18 months to 1 month at a large insurer.
How We Compare
The full picture, at a glance
| Adapts | AI Coding Agents | Manual Discovery | Doc Platforms | Context Tools | |
|---|---|---|---|---|---|
| Scope | Entire enterprise portfolio (hundreds of repos) | Single file or repo | Point-in-time snapshot | Repo by repo | IDE workspace |
| Context source | Deterministic code analysis + AI | Probabilistic inference | Human interviews + manual tracing | Generated documentation | Conversations and tickets |
| Audience | CIOs, architects, developers, PMs | Individual developers | Consulting team | Individual developers | Individual developers |
| Legacy languages | COBOL, Java, C++, Python, Go, and many more | Modern languages | Language-agnostic (manual) | Modern languages | Language-agnostic (search) |
| Speed | Minutes | Real-time (per file) | Months | Hours to days | Real-time (search) |
| Persistence | Continuous, every commit | Per session | Decays from day one | Periodic updates | No persistence |
Deep Dives
Category by category
See it on your codebase
Adapts on your code. On your terms.
Start with a 30-minute demo, or a one-week trial on your own code, on-prem and in your control.