In-House Generative AI

Own your models with privacy, cost control, and performance—deployed where your data lives (cloud, VPC, or on‑prem). We select the right base models, add retrieval over your sources, and fine‑tune only where it clearly helps. Guardrails, approvals, and full traces make actions explainable and reversible, while evaluation suites track quality, drift, and cost over time. The result is a dependable stack you can operate and audit—not a black box.

Why it matters

Building gen‑AI on rented infrastructure creates blind spots: data leaves your control, costs spike without warning, and performance drifts without telemetry. Running in-house means rightsized models, deterministic guardrails, and transparency. We make sure every generation is grounded in approved sources, citations are automatic, and the full trace is available for audits or support. When compliance or trust is on the line, you need tooling that you control end-to-end.

What we do

Private model hosting

  • Serve large or distilled models in your VPC or on-prem, with autoscaling, GPU/CPU balancing, and cost tracking per request.

Retrieval orchestration

  • Combine embeddings, keyword search, and row-level lookups so answers cite sources and respect permissions.

Evaluation pipelines

  • Scenario-based evals that track quality, drift, latency, and cost. Every release runs the suite before shipping.

Guardrails & policy

  • Safety rails, red-teaming, and approval flows so sensitive actions require human sign-off and leave an audit trail.

Fine-tuning & distillation

  • Teach models your tone, jargon, and formats. Distill heavyweight reasoning models into efficient deployable ones.

Hybrid routing

  • Run sensitive steps locally and burst heavy reasoning to external providers behind policy with full redaction and logging.

How we work

Right model for the job

  • Route deep problems to high-accuracy reasoning models; send fan-out tasks to fast, cheap models. We measure quality vs. cost, not guess.

Fine‑tuning & distillation

  • Teach models your jargon and formats. Distill heavy models into smaller, domain-specific ones for speed and privacy.

Retrieval + relational

  • Blend vector search for context with relational lookups for ground-truth facts to reduce hallucinations.

Examples

Docs & policy writer

  • Drafts SOPs and policy updates from approved sources, adds citations, and opens PRs for review. Content credentials optional.

Estimator assistant

  • Extracts structured data from plan sets, compares to benchmarks, and proposes material/labor estimates with uncertainty ranges.

Back-office agent

  • Reconciles orders and invoices, flags anomalies, and creates tickets with before/after diffs for human approval.

Handoff & Deliverables

You keep everything: model images, inference configs, retrieval pipelines, evaluation datasets, dashboards, and operations runbooks. We include risk notes and update policies in plain English, IaC for repeatable deploys, feature flags and rollback paths, and cost/latency budgets wired to alerts. We cap with a handoff workshop so your team can operate and extend the system confidently—without us in the loop.