Adapts HC3

Agentic modernization, at enterprise scale.

Adapts HC3 continuously indexes your codebases using deterministic analysis, building a living, queryable code ontology that evolves with every commit.

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Proprietary Technology

Adapts HC3

Holistic Continuous Code Context

Inside HC3

Three engines, one grounded context

Adapts HC3 is built on three engines that keep code, structure, and surrounding context in sync · together they produce one queryable model of your system.

Adapts HC3Holistic Continuous Code Context

Code Cache

The code itself

Holds the actual source, per branch and latest commit, so answers are grounded in real code rather than guesses.

Parsed source & Static Analysis
Symbols, files & snippets
Latest per Branch

Code Portfolio Graph

Where the graph lives

Stores the deterministic graph of how everything connects, from a single repo up to your whole organization.

Deterministic connection graph
Repo → org scale
Always current with commits

External Data Graph

Additional source graph

Enriches the picture with context beyond the repo: docs, tickets, pipelines, and runtime signals.

Docs, tickets & specs
CI/CD & runtime signals
Third-party systems e.g. Jira, Confluence, Notion, ADO, etc.

Delivered via Chat · MCP Servers · IDE · Coding agents · Docs · APIs

Output

Grounded outcomes, not just answers

One queryable model turns into the deliverables your teams actually use · every one traceable to source

Understand

Onboarding
Architectural Diagrams
Drift Analysis
Gap Analysis
Code Review and Analysis

Plan

Implementation Plans
Modernization Plans
Scrum Boards

Build

Implementation Suites
Test Suites
Infrastructure as Code
DevOps
Release Notes

Our deterministic ontology engine continuously indexes enterprise codebases across legacy and modern languages. It builds a living store of business logic, dependencies, and undocumented workflows.

Not repo by repo. Across hundreds of interconnected repositories, services, and infrastructure layers.

Code Portfolio Graph

From one repo to the whole estate

Products ship through many service repos. Those repos share libraries. When a library changes, the graph shows every product and service in the blast radius · before the change lands.

01
Repo GraphOne service codebase
The atomic unit · symbols, files, and call paths inside a single repository.
02
Library GraphShared packages repos depend on
Internal libraries sit alongside repos. A repo depends on many libraries · reuse and blast radius live here.
03
Product GraphMany service repos that ship together
A product is a set of service repos · ownership, contracts, and release boundaries across them.
04
Organization GraphMany products across the estate
The full map · every product linked so modernization and change span the enterprise.

Inside the graph

Structure is only the frame

Inside every product and repo, HC3 builds Mermaid-ready graphs of behavior · data models, sequences, flows, workflows, and code chains · so impact is visible before you change anything.

Example · Payments Platform

01erDiagram

DataModel Graphs

Entities, keys, and relationships from schema + code

A DataModel Graph is the living ERD of your domain · tables and objects, primary and foreign keys, and cardinalities · extracted from migrations, ORMs, and the code that reads and writes them.

When payments-api adds a column or claims-api joins the wrong table, you see the break in the model before it ships.

Payment.payment_id (PK) · customer_id, invoice_id (FK) · amount, currency, status
Customer.customer_id (PK) · account_number, kyc_tier
Invoice.invoice_id (PK) · due_date, balance_due
LedgerEntry.entry_id (PK) · payment_id (FK) · debit_credit, account_code
Relationships: Customer ||--o{ Payment · Invoice ||--o| Payment · Payment ||--o{ LedgerEntry

erDiagram

Payment

PK payment_id
FK customer_id
FK invoice_id
amount · currency
status · authorized_at

Customer

PK customer_id
account_number
kyc_tier

||--o{

Invoice

PK invoice_id
due_date
balance_due

||--o|

LedgerEntry

PK entry_id
FK payment_id
debit_credit
account_code

||--o{

02sequenceDiagram

Data Sequence

Ordered calls across services for one operation

A Data Sequence is the timed conversation between actors · UI, APIs, libraries · for a single operation such as authorizing a charge.

Shows who waits on whom, where latency and failure land, and which services join a change’s regression scope.

checkout-ui → payments-api: createCharge(invoice_id, amount)
payments-api → auth-sdk: authorizeCard(token)
payments-api → ledger-service: postPending(entry)
ledger-service → payments-api: entry_id
payments-api → checkout-ui: PaymentAuthorized

sequenceDiagram

03flowchart LR

Data Flow

Where records move, land, and fan out

A Data Flow Graph maps hops through clients, services, stores, and queues · what writes Postgres, what publishes to event-bus, and what consumes downstream.

Makes storage and async fan-out visible · so a schema or topic change shows every reader and writer in the path.

checkout-ui → payments-api → Postgres (payments, ledger_entries)
payments-api → event-bus (payment.settled)
claims-api and reporting-worker consume payment.settled
Stores vs queues distinguished · sync request path vs async fan-out

flowchart LR

04stateDiagram-v2

Workflow Graphs

Business stages the code actually encodes

A Workflow Graph is the process buried in conditionals and state machines · initiate → authorize → capture → settle · recovered from code, not from a stale Visio.

Modernization and ops follow the real lifecycle · including retries, voids, and exception paths that docs omit.

Initiate → Authorize → Capture → Settle
Authorize → Void on soft decline
Capture → Retry on processor timeout
Settle → Reconcile against ledger-service

stateDiagram-v2

Initiate

→Authorize

→Capture

→Settle

Authorize → VoidCapture → Retry

05graph TD

Code Chains

Symbol-level execution paths

A Code Chain is the call path from an entrypoint to side effects · methods, packages, and cross-repo calls grounded in the indexed code.

Answers “what runs if we change charge()?” with a deterministic chain · not an LLM guess.

PaymentController.charge()
→ CardAuthClient.authorizeCard()
→ LedgerClient.postLedger()
→ EventBus.emitSettled(payment_id)
Cross-repo edges into auth-sdk and ledger-service

graph TD

PaymentController.charge()

↓CardAuthClient.authorizeCard()

↓LedgerClient.postLedger()

↓EventBus.emitSettled()

Grounded chat

From prompt to cited answer

Chat does not guess. It reads intent, selects the right graph sources, retrieves evidence, and only then asks the LLM to answer · with citations.

Adapts ChatGrounded · HC3

If we change charge() in payments-api, what else breaks?

Grounding from HC3…Intent · sources · evidence · LLM

6 callers across Payments Platform and Claims Portal.

Primary chain: charge() → authorizeCard() → postLedger() → emitSettled().

Shared lib risk: auth-sdk · also used by claims-api.

Sources: Code Chains · Data Sequence · Repo Graph.

01
User promptReceive the question in chat
02
Understand intentClassify as blast-radius · cross-service impact
03
Pick the sourceCode Chains + Data Sequence · Payments Platform
04
Retrieve datacharge() → auth-sdk · ledger-service · 6 callers
05
Ground the LLMPrompt with graph evidence · not raw repo dump
06
RespondCited answer back in chat

Adapts HC3 Advantage

Canonical context across every system

Adapts HC3 maps your entire system portfolio: every module, every business workflow, every dependency. When you change Service A, Adapts HC3 maps the blast radius across Services B, C, and D.

How It Works

Continuous, not point-in-time

Adapts HC3 indexes on every commit. As your codebase evolves, your deterministic map evolves with it. Current as of your last commit, not your last audit.

Enterprise Scale

Hundreds of repos, one context

Cross-repo dependency mapping, business logic extraction across languages, and full-stack awareness from infrastructure to UI. Built for the complexity enterprises actually have.

Deterministic + Continuous + Enterprise Scale

Grounded context, not hallucinations

Unlike one-time analyses or single-app tools, Adapts HC3 provides continuous, deterministic understanding across your entire system portfolio. AI tools get fact-grounded context from hundreds of interconnected services · not approximations from a single repo.

Deployment

Any stack. Any perimeter.

On-premise, air-gapped, or private cloud. Fully containerized · your code and metadata never cross your boundary. Adapts HC3 runs where your estate already lives.

60+

programming languages, legacy to modern

1B+

lines of code mapped in production estates

On-prem

air-gapped, or your private cloud

Containerized

Docker, Kubernetes, or OpenShift

How It Works

One engine. Four capabilities.

Understand

Map every system across your enterprise.

Living docs, architecture and dependency diagrams, spec coverage, and accurate Q&A · all grounded in the code. A deterministic map that stays current because Adapts HC3 indexes on every commit.

Auto-generated functional and technical specs
Interactive architecture and dependency diagrams
Natural language Q&A with full system context

Architect onboarding: 3 weeks → 2 days

adapts-ai-botpublished knowledge/assistants/qa.session.yaml12s ago

Adapts QAlive

Ask anything across services, tests, and releases

Grounded answers from the org graph — never generic filler.

Adapts QAorders-qa · session

Live

If we ship orders v2, what's regression scope?

41 tests · 6 repos · 3 flags · 2 DB migrations. Grounded in call graph, release notes, and owners.

Helps with

impact before merge · blast radius in plain English
test gaps · env parity · rollout risk
cites repos, paths, and commits — org-wide

Always-current knowledge across your organization’s codebase — grounded in the system graph.

Operate

Run delivery with full system context.

Cross-service review at PR time, downstream impact, security findings, and release summaries · so agent-driven PRs never outrun human code review.

Context-aware incident resolution
Change impact analysis before deployment
CI/CD integration with system-aware guardrails

Incident resolution: days → hours

adapts-ai-botreviewed src/app/api/chat/**/route.ts3m ago

Code reviewmed

Duplicated write across two routes

Thread→conversation mapping written twice with swallowed errors.

route.ts:42–44

Suggested

single writer via shared helper
idempotency key on write
fail fast on mapping errors

Reviews and responses from adapts-ai-bot, already reasoned from the system graph.

Build

Give coding agents a map, not a guess.

Coding agents query a deterministic code graph instead of guessing, running continuously in your environment · not on the developer's machine.

Enterprise-scale context for AI coding agents
API-first integration with AI dev tools and CI/CD
MCP server integration for Claude, Codex, and any MCP-compatible agent

80% fewer agent tokens consumed

adapts-ai-botpublished integrations/mcp/servers.yaml18s ago

MCP servershealthy

Org-wide tool surface for agents and IDEs

stdio + HTTP transports · scoped to repos and roles.

Cursor · hc3-graph

You

What depends on orders-api if we change the refund rule?

Assistant

Pulling impact from the HC3 graph and cross-repo callers…

MCP · hc3-graph → impact

Covers

tool manifests · authZ per workspace
health + version pins per server
agents + editors share one registry

Delivery surfaces for agents and teams — Jira, Confluence, Cursor+MCP, Actions, and APIs wired to HC3.

Adapt

Transform legacy systems safely, at scale.

Adapts HC3 understands the business rules embedded in legacy systems. It extracts them, designs the target architecture, and generates production-ready code and test suites.

Business logic and workflow extraction from legacy code
Target architecture generation with IaC
Production-ready code generation with test suites

Discovery phase: 18 months → 1 month

adapts-ai-botproposed adr/retail-platform/0001-target.md2h ago

Architecture proposaldraft

Retail platform · monolith → domain services

6 domains · 14 services · event backbone, grounded in the estate.

0001-target.md:1–3

Covers

shape · trade-offs · run-cost
grounded in SLOs + SOX / PCI constraints
14-month plan with parallel tracks

Designs and plans to modernize at scale — grounded in the system graph.

Continuous map

Drift detection, on every commit.

Legacy estates drift faster than documentation can follow. Adapts HC3 re-indexes on every commit so your deterministic map reflects what is actually in code · not what was true at the last audit.

Built for Your Role

One platform, tailored to every team.

CIOs, architects, engineering managers, and modernization PMs each need different depth. Adapts delivers deterministic context at the right level for each.

CIO / VP Engineering

Accelerate timelines, de-risk modernization, free budget for innovation.

Architecture discovery: weeks → hours

Enterprise Architect

Understand every system, map every dependency, design with confidence.

System behavior mapping: months → days

Engineering Manager

Delivery health and team productivity, one pane.

Onboarding: 3 weeks → 2 days

Modernization PM

Plans built from the real system · sequenced phases with evidence.

Discovery: 18 months → 1 month

See it on your codebase

See Adapts HC3 on a system like yours.

30-minute technical walkthrough with an enterprise architect. No slides · a live demo on real code.

Start a free trial See the engine →