Build · AI

AI Implementation

We take AI from whiteboard to production — RAG pipelines, LLM apps and agentic systems and wired into your stack with guardrails, evals, observability and real cost controls.

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Secure by design — your data stays in your environment.

Any AI appFaithfulnessgroundedRelevancyon-topicRailGuardsafe & gatedTraceabilityloggedFeedback loopimproves
01What we build

production rag pipeline

✓ every response gated on faithfulness ≥ 0.90

The difference between a demo and a production system is discipline: permissions, evaluation, observability, failure handling, and cost controls.

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Prove it
No AI ships on vibes. Faithfulness, relevancy, precision and recall are measured on every release — gated against targets agreed up front — and we red-team the same build for jailbreaks, injection and PII leakage before it reaches a user.
Your knowledgedocs, data & systemsSafety checkblocks unsafe or private dataFinds the factspulls the right source, not guessesWrites the answerin plain languageDouble-checked before you see itaccuracy verified · safety stress-testedno made-up answers get throughAn answer you can trustand it gets smarter with every answerlearns as it goes
03Ready to run

Plugins & Agents

A library of autonomous agents you can demo today — each one a node in the constellation, each one doing real work in your pipeline.

Agentic workflowGUARDEDGrounded in your sources — no driftTRACE · WHO DID WHATagent-1retrieved your sourcesagent-2checked faithfulnessagent-3wrote the answer

Claude Code Plugins

FreeClaude Code

Our open marketplace of Claude Code plugins — drop-in slash commands, subagents and skills that plug Pangaea workflows straight into your editor.

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AI Audit

eval + security

Scores faithfulness, relevancy and recall, and red-teams for jailbreaks, injection and PII leaks.

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Prod RAG Pipeline

rag

Crawl → chunk → embed → retrieve, with guarded tool calls on every hop.

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FAQ

Questions, answered

What’s the difference between an AI demo and a production AI system?
A demo wows in a meeting; a production system stays accurate on the 1,000th call. We gate every answer on faithfulness, red-team it for prompt injection and PII leaks, and ship it with observability and real cost controls.
How do you stop a RAG chatbot from hallucinating?
We ground answers in your own data with hybrid retrieval and reranking, score faithfulness on every response, and block anything below the gate — so no made-up answers reach the user.
How long does it take to ship an AI agent?
We start with a 30-minute Adopt Assessment to scope one use-case and the eval that defines “done”, then ship a gated, production-ready version in weeks — not a quarter.
Can it run in our own cloud and keep data private?
Yes. It’s secure by design — it runs inside your environment, your data stays yours, and PII is redacted in the pipeline.

Put a gated AI system in production.

Book a 30-minute Adopt Assessment — we scope one use-case and the eval that defines done.

Book an Adopt Assessment