Not a vendor. A partner who's solved genuinely hard AI problems — and can solve yours. From custom solutions to full infrastructure.
Every engagement starts with understanding your business. We bring the AI depth — you bring the domain knowledge.
Tailored AI tools built for your specific business needs. RAG pipelines, knowledge graphs, document processing, agent systems, and more. We don't build demos — we build production systems.
Run Nexus tools on your own infrastructure with your branding. Full control over data, models, and deployment. Ideal for businesses handling sensitive information or requiring regulatory compliance.
The backend that makes AI work with your data. KV cache optimization, embedding pipelines, retrieval systems, model fine-tuning, and grammar-constrained outputs. The hard engineering that most teams can't do in-house.
AI readiness assessments, implementation roadmaps, and team training. Understand what AI can and can't do for your business, then build a plan to get there.
No bloated SOWs. No six-month discovery phases. We move fast and ship real value early.
Understand your business, data, and goals. Identify where AI creates the most value.
Design the solution. Choose models, pipelines, and infrastructure that fit your constraints.
Iterative development with frequent check-ins. You see working software early, not just status reports.
Launch to production with monitoring, documentation, and ongoing support as needed.
Every engagement teaches us something. Here are three that shaped how we think about AI.
Client: A mid-size law firm processing thousands of complex documents — scanned pages, handwriting, stamps, checkboxes, and flipped images.
Problem: Existing AI tools required expensive one-pass extraction that lost critical nuance. Accuracy wasn't good enough for legal work, and the cost per document was prohibitive at scale.
Approach: Reframed as an economics problem. Instead of extracting everything upfront, loaded documents into a persistent KV cache so the team could ask unlimited follow-up questions at near-zero marginal cost.
"Turned an accuracy problem into an economics problem by flipping the cost structure of LLM inference."
Client: A content platform serving media professionals who needed to see how their ideas, interviews, and content connected.
Problem: Users could store documents but couldn't visualize relationships between concepts. Traditional search wasn't enough — they needed to see the structure of their knowledge.
Approach: Integrated a multi-dimensional knowledge graph with RAG. An AI agent captures ideas, interviews, and remixes content while users visually navigate their knowledge.
"Not just storing documents — seeing how everything relates."
Client: A Y Combinator-backed AI inference startup building privacy-first AI with verifiable guarantees.
Problem: Needed a production-ready client library with broader browser support, cleaner developer experience, and verifiable privacy guarantees via secure enclaves.
Approach: Refactored the client library, expanded platform support, and improved the developer interface while preserving the encrypted-before-sending architecture.
"Expanded platform support while preserving the encrypted-before-sending architecture."
We build general tools and configure them for your specific vertical. The same depth, applied to your domain.
Document review, contract analysis, compliance
Content intelligence, knowledge management
Clinical documents, privacy-first processing
Risk analysis, regulatory compliance, reporting
Every engagement starts with a conversation. Tell us about your business and we'll tell you what's possible.
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