Graph-grounded enterprise AI — every answer cited, every decision traceable, from your documents to your codebase to your AI estate. We design, build and operate it for regulated, mission-critical environments — from first architecture review to 24/7 operations.
Pilots stall. Models hallucinate on real data. Security and compliance teams block deployment. The gap isn't ambition — it's engineering discipline.
Demos built on curated data break down against real enterprise systems, edge cases and volume.
Without retrieval, evaluation and guardrails, generative systems hallucinate — a non-starter for regulated workflows.
Data residency, access control and audit requirements are treated as an afterthought instead of the foundation.
Our answer to that gap is one idea applied everywhere: we turn your reality — documents, codebase, or your whole AI estate — into an explicit knowledge graph, then make the AI reason over it. Outputs trace to something real, not a model's guess. That single competency powers everything we build.
Not "we prompt carefully" — a structural rule: cite the source, or don't answer. The anti-hallucination stance regulated buyers actually require.
One competency — parse reality into a graph, reason with GraphRAG — reused from retrieval to code intelligence to AI governance. A platform, not a menu.
Built for regulated, high-stakes environments: evaluation harnesses, audit trails, human-in-the-loop and security review as the foundation, not an afterthought.
The control layer for enterprise AI. As you deploy more models and autonomous agents, the missing piece isn't another chatbot — it's the runtime that governs them all by reach and permission, not keywords.
One partner across the full lifecycle — from architecture decisions to production operations.
We assess readiness and define the architecture before you invest in build.
We engineer secure, production-grade systems — not prototypes.
We operate, evaluate and improve what's in production, continuously.
Every engagement is built on the same disciplined architecture — grounded, governed and observable at every stage.
Secure connectors bring in documents, databases and SaaS systems of record, with incremental sync so the platform stays current.
RAG and GraphRAG pipelines turn enterprise knowledge into retrievable, cited context — combining vector search with graph reasoning.
Model routing and agent orchestration decide which model handles a request, and which tools it's allowed to call via Model Context Protocol.
Guardrails, automated evaluation and audit trails run on every response before it reaches a user — not as an afterthought.
MLOps/LLMOps keeps the system observable in production — monitoring cost, latency and quality, and improving it continuously.
Our graph-grounded capabilities lead — code, retrieval and governance. The rest builds on the same production discipline: grounded, governed and measurable.
Repository knowledge graphs for due diligence, modernization and an AI software architect.
Learn moreGrounded, cited answers over enterprise knowledge — the graph that makes retrieval traceable.
Learn moreGovern every AI app, agent and model by reach and permission — on an AI knowledge graph.
Learn moreAutonomous, multi-agent systems that use tools and automate workflows.
Learn moreCustom copilots, RAG and fine-tuned LLMs built for the enterprise.
Learn moreAutomate document processing with AI-driven pipelines.
Learn moreChat and voice agents for support, sales and operations.
Learn moreSecure LLMs deployed on-premise or in your own cloud.
Learn moreVision and multimodal systems that inspect and detect.
Learn moreMonitor, govern and continuously improve models in production.
Learn moreRepresentative engagements across regulated and high-scale enterprise environments.
Branches and contact centers now get grounded, cited answers instead of manual lookups — the full story is below.
Staff spent hours searching policies and product documentation, with generic chatbots giving unreliable, uncited answers.
Built a RAG-grounded copilot with GraphRAG for cross-document reasoning, strict guardrails, PII redaction and an automated evaluation harness.
A trusted, cited assistant adopted across branches and contact centers.
Every system we build is designed for the review it will face — from security, legal and compliance teams — before it earns the review from users.
Encryption in transit and at rest, isolated environments and least-privilege access by default.
Role-based access, approval workflows and clear ownership over every model and agent.
Architectures designed around data residency, retention and industry-specific regulation.
We advise, build and scale AI solutions — AI strategy & consulting, AI product engineering, agentic and generative AI systems, RAG/GraphRAG platforms, intelligent automation, and managed AI services including MLOps/LLMOps.
Fill out our contact form or reach out by email. We'll schedule a working session to understand your systems and constraints, then propose an architecture and scope.
We start with your data and constraints, not the model. That means architecture review, a scoped proof of concept, evaluation against real workloads, then production hardening and operations.
Yes. We design for data residency, access control and auditability from day one, and support on-premise or private-cloud deployment where required.
A scoped proof of concept typically takes 3–6 weeks. Full production deployments range from 2–4 months, depending on integration complexity. We provide a detailed roadmap during scoping.
Let's talk about where AI can move the needle for your enterprise — from architecture to operations.
