Integrate GPT, Claude & Open-Source LLMs into Your Business
Production-grade LLM wiring — vendor APIs, private deployments, RAG, evaluations, and guardrails tuned for reliability rather than demo novelty.
Integration playbook
We map prompts to measurable outcomes, ground responses with retrieval where facts matter, and enforce schemas when downstream automation consumes outputs.
Privacy tiers span enterprise API contracts, redaction pipelines, air-gapped open weights, or hybrid routing between cheap and premium models.
Operate with confidence
Hallucination risk drops via eval harnesses, human escalations, caching of stable answers, and tracing so regressions surface before users do.
Cost control leverages batching, router policies, and selective fine-tunes only when retrieval alone stops scaling.
What we deliver
Vendor APIs
OpenAI, Anthropic, Google — quotas, retries, observability.
Open weights
Llama, Mistral on VPC/GPU — LoRA when domain fit demands it.
RAG systems
Chunking, embeddings, re-rankers, citation UX.
Fine-tuning
Dataset hygiene, eval splits, safe rollback paths.
Safety layers
Moderation, PII gates, policy-aware prompting.
Production ops
Latency budgets, fallbacks, structured logging.
Questions we hear
often.
Which model family first?
Prototype on frontier APIs for speed; migrate workloads to open weights once economics or residency rules crystallize.
Data residency?
Enterprise tiers + redaction + optional self-hosted inference — architecture follows compliance obligations.
Cost outlook?
Build fees typically mid five figures depending on surfaces; run-rate trimmed via caching, routers, and batch flows.
Reduce hallucinations?
Ground truth corpora, validation schemas, secondary checks, and continuous eval dashboards.
Customization depth?
From prompt packs → RAG → adapters/full fine-tunes — stepped investment tied to ROI checkpoints.
Ready to scope your build?
Tell us about timelines, integrations, and success metrics — we'll reply with a concrete path forward.
Schedule Growth Call →