AI Implementation

Measured, guarded, monitored—so it keeps working.

EvalGuardrailsMonitoringCost control
LLM workflowsRAGAutomation

What you get

  • Evaluation harness with regression suite

  • RAG design + indexing strategy (if needed)

  • Prompt versioning + rollback plan

  • Observability: quality metrics + cost tracking

  • Guardrails: input validation + output filtering

  • Integration with existing systems

Reliability model

01

Data

Clean test sets, representative examples, and edge cases documented.

02

Eval

Automated checks for accuracy, relevance, safety, and regression.

03

Deploy

Versioned prompts, gradual rollouts, and rollback plans in place.

04

Monitor

Track quality (accuracy, deflection), latency, and cost per request in production.

Common use cases

Support copilot

Answer customer questions with context from docs, tickets, and knowledge base.

Internal search

Semantic search across documents, code, and internal tools.

Workflow automation

Extract, classify, and route data from emails, forms, and documents.

Reporting & analysis

Generate summaries, insights, and structured data from unstructured sources.

Ship AI that works.

Tell us what you're building. We'll respond with a plan that includes evaluation, guardrails, and monitoring.

AI security review included: Security Testing · Cybersecurity