MergeLoom

Give AI Agents Whole-System Context

Context Engine maps your entire architecture—from documentation to cross-repository API relationships. It packages everything into a secure knowledge base, keeping the AI's context fresh as your code changes so it never has to guess.

After the first index, refresh jobs estimate changed files and AI credits before updating stale context, so agents stop rediscovering the architecture on every run.

Approved SourcesSelf-Updating VaultCross-Repository GraphScoped Context PacksRun Evidence
Context Pipeline
Product Map
  1. Step 01
    Select Sources
  2. Step 02
    Build Vault Docs
  3. Step 03
    Refresh Changes
  4. Step 04
    Attach Context Pack

What Breaks When AI Lacks Fresh Context

AI coding output completely falls apart when every run has to guess at your architecture, stale documentation, and service relationships.

Prompt Guesswork

Manual prompting depends entirely on who writes it, meaning AI constantly misses rules hidden in docs, prior runs, or adjacent services.

Repeated Discovery

Contextless AI burns expensive tokens re-reading files, routes, and service dependencies on every single run instead of using reusable system knowledge.

Stale Repository Knowledge

Merged changes quickly leave indexed documentation behind unless your AI context automatically refreshes against the latest repository state.

Narrow Codebase View

A run locked to a single repository blindly misses critical context from your shared packages, APIs, background workers, and downstream services.

AI That Actually Understands Where Your Microservices Meet

Stop coding in multi-repository blind spots. Context Engine indexes your ecosystem once and self-updates as code changes. When a run starts, the agent queries it to grab a surgical, bounded context pack for that exact task—giving you cross-repository intelligence with zero token waste.

Approved Sources
Scoped
Refresh Model
Delta
Context Pack
Bounded
Run Evidence
Source-Linked
Approved Sources

Only Trusted Inputs

Context Engine starts with repositories, docs, and knowledge sources your team explicitly approves.

Repositories

Target repos, related services, shared packages, workers, and APIs.

Documentation

Architecture notes, repository docs, AGENTS.md, and selected Markdown.

Confluence

Selected pages join the same evidence path as code context.

Access Rules

Include, exclude, and scope controls decide what enters the vault.

System Map

Context Vault

Reusable architecture memory connects files, symbols, APIs, events, docs, and service relationships before a run starts.

Service Links

Upstream APIs, downstream services, packages, events, and background jobs.

Source Evidence

Exact paths, commits, snippets, docs, and indexed baseline state.

Rules And Commands

Repository guidance, validation commands, file patterns, and coding rules.

Refresh Delta

Changed and deleted files update stale context without full rediscovery.

Context Vault

Indexed source evidence, relationship graph, summaries, commits, and confidence.

Links
API + event
Scope
Job-bound
Budget
AI-credit capped
Run Brief

Bounded Context Pack

Agents receive only the context needed for the job, with source reasons attached.

Architecture Brief

Relevant services, contracts, and dependencies arrive before code changes.

Scoped
Source Reasons

Every packed context item explains why it was included.

Explained
Commit Evidence

Indexed commits and stale-context state stay visible.

Traceable
Token Boundary

Context is selected and bounded rather than dumped into the run.

Controlled
Discovery Cost
Reduced
Architecture View
Whole-system
Refresh Control
Budgeted
Review Context
Source-linked

Why This Beats Prompt Stuffing

Prompt stuffing blindly dumps thousands of lines of code into a chat window and hopes for the best. Context Engine does the opposite—it acts as an on-demand search engine, surgically pulling a precise, token-efficient context pack only when the agent needs it.

Reusable System Memory

Architecture rules, API contracts, and indexed commits are stored globally so they are instantly available across every run instead of trapped in one chat window.

Smart Delta Syncs

After a one-time baseline index, the system self-updates by strictly tracking added, modified, and deleted files instead of processing everything from scratch.

Cross-Repository Mapping

Sprawling repositories are indexed together, making shared packages, downstream services, and event pipelines visible before coding ever starts.

Audit-Ready Receipts

Every context pack carries exact source paths, confidence metrics, and indexed commit histories right to the PR for easy human review.

The Old Way: Blind Prompting & Broken Links

  • Throwaway Context
  • The Bloated Blind Box
  • Single-Repository Blind Spots

The MergeLoom Way: Connected Ecosystem Intelligence

  • Reusable System Memory
  • Smart Delta Syncs
  • Cross-Repository Mapping
  • Audit-Ready Receipts

Total Architectural Visibility

Your complete system knowledge in one place. Instantly review indexed code sources, system coverage, and active run context packs right from the UI.

MergeLoom Context Vault dashboard showing indexed sources, vault state, and system knowledge.
Context Vault dashboard showing indexed sources, system coverage, vault state, and reusable product knowledge.
MergeLoom Context Vault detail screen showing reusable context attached to AI coding runs.
Vault detail view showing source-linked context evidence attached to coding runs.

Lock Down Your Code And Your Budget

No runaway cloud bills or unapproved repository access. Scope what your AI sees, control refresh schedules, and track every dollar of context spend in real time.

  • Approved Sources

    Only configured repositories, docs, related services, include patterns, and context sources are used for indexing and run context.

  • Scoped Worker Access

    Workers receive authorized repository IDs, and runtime search rejects context outside that job scope.

  • Refresh Budget Control

    Enable auto refresh with a monthly AI-credit budget, so stale context updates inside cost limits.

  • Private Deployment Boundary

    Self Hosted runs Context Engine beside your worker with a private vault volume and repository cache. We never train models on your data.

The Ultimate Shortcut For Complex, Multi-Repository Work

Stop letting AI guess at your architecture. This is built specifically for tasks that need an exact, real-time understanding of your team conventions, validation rules, and cross-service dependencies.

Multi-Repository Products

Index related repositories together so runs understand shared packages, upstream APIs, workers, events, and downstream services.

Architecture-Sensitive Work

Attach routes, dependencies, symbols, patterns, docs, and validation expectations before implementation starts.

Review-Heavy Teams

Show which vault docs, source paths, commits, and context-pack snippets shaped a PR/MR.

Confluence-Driven Teams

Bring selected Confluence pages into the same indexed context path as repository knowledge.

Large Codebases

Avoid full rediscovery on every run with reusable vault docs, changed-file refresh, and scoped context packs.

Quality Agent Workflows

Give investigation, validation, and review agents the same run evidence before they judge output.

Context Engine FAQ

Is Context Engine Just RAG?

No. It uses search, but the product is not a generic retrieval wrapper. Context Engine indexes approved sources into vault documents, builds graph evidence across repositories, services, and APIs, and assembles scoped context packs for runs.

Doesn't This Make Indexing Expensive?

The first index depends on repository size and selected sources. After that, Context Engine estimates added, modified, and deleted files and refreshes changed context instead of blindly rebuilding everything. Small refreshes are cheap; larger refreshes show AI-credit and time estimates before they run.

How Does It Stay Up To Date?

Enable auto refresh with a monthly AI-credit budget. MergeLoom checks vault state, detects merged changes against the indexed baseline, and refreshes stale context so new runs use recent system knowledge.

Does It Use Related Repositories Outside The Target Repository?

Yes, without opening write access. Approved related repositories are indexed into the same vault so Context Engine understands service, package, API, event, and dependency relationships around the target repository. Snippets from another repository still require explicit approval as run context.

Does It Use AGENTS.md And Repository Docs?

Yes. Repository guidance, Markdown docs, architecture notes, context repositories, selected Confluence pages, and approved documentation sources shape runs.

Do Teams Control Sources?

Yes. Teams choose connected repositories, enable or disable Context Engine indexing per repository, add Confluence page context, and set include/exclude patterns.

Does It Work With Self Hosted?

Yes. The Helm chart runs Context Engine beside the self-hosted worker with a private vault volume, repository cache, worker/internal tokens, and tenant/workspace runtime scope.

Explore Related Product Areas

Start Free With No Risk

Pay For Outcomes, Not Seats

Run MergeLoom on scoped work before rolling it out. You only pay when a run opens a PR/MR for review, not for seats or tickets that stop before handoff.

Cloud

50 Free PR/MR Runs

Then From £4 Per PR/MR

Self Hosted

50 Free PR/MR Runs

Then From £2 Per PR/MR

Paid Outcomes

Only PR/MR Runs Count

No PR/MR, No Run Charge

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  • Pay For Outcomes
  • No Lock-In Contracts
  • No Credit Card Required (Self-Hosted)
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No PR/MR, No Run Charge · No Seat Pricing · Human Review Stays In Control

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