Services
Generative AI built around workload and evidence.
From retrieval-augmented assistants to long-document reasoning — with evaluation, cost, and security treated as first-class requirements.
Evidence note: Generative AI proof links to Continuum1-9B and public research notes. Benchmarks are protocol-specific snapshots, not universal guarantees.
Problems we address
Context window bottlenecks
Documents and histories that break standard attention cost curves.
Hallucination risk
Assistants that answer without grounded retrieval or audit trails.
Uncontrolled spend
Token costs that explode without caching, routing, or eval gates.
Capabilities
Long-context systems
Architecture and application work informed by Continuum1-9B research when sequences are the binding constraint.
RAG & knowledge assistants
Retrieval design, chunking strategy, and answerability evaluation.
Guardrails & ops
Logging, red-team checks, and rollout plans that match enterprise risk.
How engagements typically run
Workload definition
Documents, latency, privacy, and success metrics.
Approach selection
RAG, fine-tune, long-context, or hybrid — with tradeoffs.
Build & evaluate
Representative questions and failure modes first.
Production path
Serving, monitoring, and cost controls.
Related proof & next steps
Related services
Questions
No. Continuum is relevant when extreme context and linear compute matter. Many apps are better served by retrieval plus a standard model.
Talk with us about generative ai.
Share constraints and goals — we will respond with a technical discovery path.