Agentic context layer

Make your code AI-ready

You can't move forward with AI if you're stuck in legacy. We turn opaque codebases into a Knowledge Base your teams, tools, and AI agents can actually use.

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Why this matters

If you're rolling out AI coding tools on a legacy, large, complex codebase, you have a code-understanding problem.

Developer-facing AI tools are stuck

Copilot, Cursor, Claude Code adoption is flat. Developers say the suggestions are wrong or generic. The tools are only as smart as the context you feed them.

Agents need to query your system

Internal agents and MCP servers need to know what your application actually does. Filenames, READMEs, and a folder structure aren't enough.

AI refactoring needs ground truth

AI-assisted modernization is only safe when grounded in real system behavior. Pattern-matching against unfamiliar code is how you ship regressions.

How we do it

Three layers, working together

Static analysis for structure. GenAI for scale. Senior engineers for trust. We run all three to build the Knowledge Base your tools need.

Layer 01

Deterministic analysis

Our proprietary engine maps the structure of your codebase: dependencies, entry points, data flows, dead code, cross-repo relationships. The structural foundation of your Knowledge Base, the ground truth everything else gets anchored to.

Knowledge Base · structure
acme.platform1.2M LOC
├─ billing.PricingEngineINDEXED · 12 rules
├─ billing.LegacyRepoINDEXED
├─ checkout.OrchestratorINDEXED · 8 flows
├─ payments.BridgeServiceSME notes
└─ admin.ReportsServicedead code
847 modules · 124 flows · 318 rules · MCP-ready
Layer 02

AI for scale

Anchored in static analysis, our AI agents document the flows, summarize the modules, and link business logic to the code that runs it. The Knowledge Base's documentation layer, generated at scale and grounded in real structure.

AI suggestion
Cursor · PricingEngine.computeRate()
// Without context layer:
- return amount * region.adj * (1 - tier.discount);
// With Swimm context (rule indexed):
+ // Tier discount BEFORE regional adjustment
+ // (compliance rule, captured by senior engineer)
+ const tierAdjusted = applyTierDiscount(amount, tier);
+ return applyRegionalAdjustment(tierAdjusted, region);
318 indexed rules grounding suggestions
Layer 03

SMEs and AI experts

Senior engineers add the tribal knowledge that isn't in the code: ordering constraints, compliance rules, threading assumptions, the "don't touch this" notes. The Knowledge Base's last mile, encoded so your AI tools surface it, not bury it.

Captured knowledge
Senior platform engineer
PricingEngine.computeRate() · line 152

Tier discount must be applied BEFORE regional adjustment. Compliance rule, not a math optimization. Any refactor that swaps the order passes tests and fails audit. Surfaced to AI agents as rule.compliance.tier_order.

Senior platform engineer
payments.BridgeService

Single-threaded by design. Old contract with the payment processor. Tested at 50 TPS, breaks at 200. Agents must not parallelize this path. Locked.

Engagement model

Your Knowledge Base, delivered

Four stages. Fixed price per stage. Commit one step at a time, with validation evidence at every one.

01

Assessment

Snapshot of your codebase and AI-readiness gap. What's documented, what isn't, where your AI tools fail today. Scoped plan, risks, and success criteria.

What you receive
  • Codebase snapshot and AI-readiness audit
  • Scoped plan
  • Risk register
  • Locked success criteria
02

Specification

Extract the business logic, critical flows, and system behavior your AI tools and agents need. Documented, queryable, with validation specs locked.

What you receive
  • Architecture and dependency maps
  • Extracted business logic
  • Documented critical flows
  • Validation specifications
03

Modernization

Build the Knowledge Base. Index your codebase, capture tribal knowledge, expose it via MCP, and wire it into your AI tools.

What you receive
  • Validated Knowledge Base
  • MCP server and endpoints
  • AI tool integration
  • Validation evidence
04

Enablement

Keep the Knowledge Base current as your code evolves. Playbooks, training, and update tooling, handed off to your team.

What you receive
  • Knowledge Base maintenance playbooks
  • Team training
  • Update tooling
  • Queryable knowledge base

Each stage builds on the last. Fixed price, with validation evidence at every step.

What you get

A Knowledge Base, not a stack of files

Every deliverable lives in one place. Yours to keep, queryable by your tools.

Delivered change

  • Validated Knowledge Base
  • MCP server and endpoints
  • Integrations with Copilot, Cursor, Claude Code
  • Agent-ready context API

System understanding

  • Architecture and dependency maps
  • Dead-code analysis
  • Extracted business logic
  • Critical flow documentation
  • Queryable by MCP

Enablement assets

  • Knowledge Base maintenance playbooks
  • Team training
  • Update tooling
  • Audit-ready artifacts where relevant

From SDLC to ADLC. With proof.

Get in touch with our team. We'll talk through your codebase, your AI tooling, and the right path to a Knowledge Base your tools can actually use.

Get in touch