Knowledge-Augmented Generation
The end of guessing how your product works.
Muninark ingests everything your product has ever recorded and serves it back as verifiable answers: every claim cited to its source, rated for confidence, and correct for the version you ask about. For your people and your AI agents alike.
Why does invoice rounding differ between v4 and v6?
HIGH · 91%v4 keeps dual rounding to honor the 2019 contract terms; the architecture board decided to retain it until every customer left v4. The fallback was removed in v6.0 once migration completed, so v6 rounds once, at line level.
Why was the export limit set to 500?
INSUFFICIENTNo decision record or discussion found. The value predates the tracked history, imported 2017-01-09. Muninark declines to answer rather than guess.
SAMPLE ANSWERS · RENDERED AS SERVED
Grounded, not plausible
Answers come from your record: commits, tickets, decisions, documents, recordings, deployment configuration. Never from a model's general impression of how software usually works.
Verifiable to the source
Every answer carries an evidence pack: typed citations with source system, record id, and date. Any claim can be checked against the artifact it rests on.
Honest about limits
Calibrated confidence on every answer: High, Medium, Low, or Insufficient. When the record cannot support a claim, Muninark says so instead of improvising one.
Temporality
Answers as of any version. No one else has the vocabulary.
Facts in Muninark carry validity in time. When your customers run different versions at once, one question has several true answers, and Muninark keeps every one of them, dated. Across the market, answers describe the present; Muninark answers for any point in your product's history.
FACT TIMELINE · invoice rounding
Support answers for the customer's actual version. Nobody debugs v6 with facts from v4.
The substrate
Store anything. Connect everything.
A state-of-the-art ingestion pipeline turns whatever record your organization keeps into one governed, temporal knowledge graph with hybrid retrieval over entities, relationships, and time.
Temporal knowledge graph
entities · relationships · time
Under the hood
AI at both ends. A graph you can audit in the middle.
The temporal knowledge graph is the visible part. Around it, Muninark is an AI system through and through, and the graph is what keeps that AI accountable.
01 · AI THAT READS
A multi-stage LLM pipeline reads every artifact: it extracts entities and relationships, links decisions to the commits that carry them out, composes knowledge units from scattered records, dates each fact's validity, and notices when new evidence invalidates old facts.
02 · EXPERTS WHO VOUCH
What the models extract, your experts can confirm or correct in a review portal. The memory is machine-built and human-vouched, and the corrections become part of the record too.
03 · AI THAT ANSWERS
At question time, hybrid retrieval over graph, full-text, and vectors assembles the evidence, and a model writes the answer from that evidence alone: cited, scored for confidence, refused when the record is thin.
Context engineering
The layer no model replaces
Model intelligence rises on every benchmark, and none of it arrives knowing your product. Muninark does not replace reasoning models; it is the layer that makes their reasoning land on your facts: context engineering built as a platform, the governed layer between your record and whatever model you run. Deliberately model-agnostic, so the models that read and answer can be swapped as the state of the art moves.
Better models don't compete with this layer. They draw on it better. Every gain in model quality compounds through the same graph.
Agent-native
One graph over MCP, three audiences it serves
Muninark is an MCP server. Claude Code, Copilot, Cursor, and any MCP-capable agent connect directly and receive the same answer object a person gets: cited, confidence-rated, version-scoped. Everyone else hands agents raw context and hopes; Muninark hands them verified answers. Packaged skills and supervised agent processes build on the same foundation. What those answers unblock differs by who is asking.
For development · coding agents and IDE assistants
Historical why. The agent learns why the code is the way it is before it edits it.
Impact analysis. What a change touches across modules, contracts, and versions, checked before the diff.
Change conformance. Did the shipped change follow the recorded decision.
Archaeology. Dead code and odd configuration explained from the record, not guessed at.
Engineer onboarding. Cited answers from day one, without pulling seniors off their work.
Refactors and rewrites. Which behaviors are load-bearing and which are accidents of history, known before anything is dropped, from partial cleanups to a full language migration.
For the business · chat, Slack, and business agents
Support at version. Answers scoped to the customer's actual release, not the latest one.
Audits and diligence. Requirements traced to the commits and decisions that implement them, with evidence on record.
Risk and ownership. Who knows each area, and where knowledge is one resignation from gone.
Knowledge discovery. Prior art and past attempts surfaced before anyone repeats them.
Deployment context. What runs where, configured how, for which customer.
For autonomous agents · delegated processes
Delegated processes. Multi-step workflows run by agents under supervision, with approvals where they matter.
Conformance sweeps. After a release, agents check shipped changes against the recorded decisions.
Standing watch. Ownership thinning and knowledge concentration flagged on a schedule, not discovered in an exit interview.
Same evidence, same audit trail. An agent's work carries the citations and confidence a person's answer would, and lands in the same hash-chained log.
The whole platform
Temporal knowledge graph
Entities, relationships, and validity in time. Bi-temporal facts with full invalidation history.
Evidence packs
Typed citations on every answer: source system, record id, retrieval time.
Calibrated confidence
High, Medium, Low, Insufficient. Refusal is a designed answer, not a failure.
Hybrid retrieval
Graph traversal, full-text, and vector search combined, tuned for evidence quality.
Nine question classes
Historical why, impact, conformance, discovery, onboarding, archaeology, ownership, deployment, regulatory.
Answer-level governance
Deny-by-default entitlements, hash-chained audit of every answer, redaction, GDPR deletion.
Operator consoles
An ops console for the pipeline and a review portal where your experts curate the graph.
Your environment
Licensed software installed where your data lives, with OIDC single sign-on.
Where the category stops, Muninark starts
Context layers, enterprise search, agent memory, code intelligence, internal Q&A, RAG frameworks: each solves a slice of the problem. None of them answers for a version, with evidence, on the record.
Curious what Muninark would recover from your product's record? It starts with a conversation.
Muninark is licensed enterprise software, installed in your environment and governed at the answer level. Built for organizations with mature products and real compliance obligations, not a self-serve app.