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Applied AI & GenAI that teams can trust

From strategy to safe deployment—copilots, RAG, and tuned models with evaluation, guardrails and cost control.

AI architecture concept

Why it matters

AI creates leverage—if it’s safe, measurable and sustainable. We ensure real KPIs, not novelty demos.

  • Measurable evals (quality, latency, safety, cost).
  • Guardrails: data privacy, prompt policy, red-team tests.
  • FinOps for tokens/throughput & autoscaling.
  • Rollouts with fallbacks and human-in-the-loop.

What we deliver

Strategy & discovery
  • Use-case backlog & value assessment
  • Data/architecture readiness & gaps
  • Operating model, skills & governance
Copilots & agents
  • Task automation & assistants (support, sales, ops)
  • Tool use (functions), memory, multi-step plans
  • Guardrails: PII/PHI, prompts, policy enforcement
RAG & knowledge
  • Chunking, embeddings, vector/rerank hybrids
  • Citations, provenance, hallucination reduction
  • Freshness SLAs & search quality evals
Modeling & tuning
  • Open & proprietary models (OpenAI, Claude, Llama, Mistral)
  • Prompt engineering, adapters/LoRA, distillation
  • Safety, toxicity & bias tests
MLOps & LLMOps
  • Feature stores, registries, experiments
  • Eval harnesses (quality, cost, latency, safety)
  • Observability, rollout policies, fallback trees
Security & compliance
  • Data loss prevention, redaction & masking
  • Audit trails, secrets & key management
  • GDPR/HIPAA/SOC2 controls

Reference GenAI pattern

Retrieval-augmented generation with policy, redaction and evaluation. Built for citations, monitoring and safe fallbacks.

  • Ingest ➝ chunk ➝ embed ➝ store (vector + structured)
  • Query ➝ rerank ➝ compose prompt ➝ generate ➝ cite
  • Eval harness & telemetry: quality • cost • latency • safety
GenAI RAG pattern
+30%
Productivity lift (target)
−25%
Time-to-resolution
Eval-first
KPIs for safety/quality/cost

Models & tools

OpenAIAnthropicLlamaMistralLangChainLLamaIndexVector DBs (Pinecone/pgvector/FAISS)Hugging FaceWeights & BiasesMLflowdbtAirflowDatabricksSnowflake CortexVertex AIAzure AI StudioOpenTelemetryPrometheus/Grafana

FAQs

Which model should we use?

We’re model-agnostic. We evaluate on your data for quality, latency and cost, then recommend a fit-for-purpose mix.

How do you prevent hallucinations?

Citations, retrieval reranking, response validation, eval harnesses and safe fallbacks with human review.

Can you run fully private?

Yes—private endpoints, on-prem inference or VPC-scoped SaaS depending on constraints.

What about compliance?

Data minimization, redaction, audit logs and policy enforcement aligned to SOC2/ISO 27001 and sector regs.

Ready to ship AI safely?

Share a problem and a dataset; we’ll propose the smallest pilot that proves value.

Tell us about your goal

We’ll follow up with next steps and a tailored approach.