AI Integration & Automation
We help businesses leverage large language models, RAG pipelines, and intelligent automation to reduce manual work and create smarter products. From prompt engineering to production deployment, we ensure your AI features are reliable, safe, and measurable.
What's Included
Our Process
AI Opportunity Audit
We review your existing product and workflows to identify where AI creates the highest ROI — not where it sounds exciting.
Proof of Concept
A focused 2-week POC that proves the core AI behaviour works in your context before we build production infrastructure.
Production Engineering
Scalable AI pipelines with evaluation frameworks, monitoring, guardrails, and cost controls built in from the start.
Evaluation & Iteration
We build eval datasets, run automated quality checks in CI, and iterate based on real user feedback until the feature meets your success criteria.
Case Studies
RetailBot AI
E-commerce AI assistant that handles customer queries, product recommendations, and order tracking.
LegalDocs Automator
AI-powered document generation platform for law firms that reduces contract drafting time by 80%.
Frequently Asked Questions
We are model-agnostic but have the deepest production experience with Claude (Anthropic), GPT-4 (OpenAI), and Gemini (Google). We recommend models based on your specific use case, latency requirements, and cost targets.
We design multiple layers of control: input/output filtering, structured output constraints, confidence thresholds, human-in-the-loop escalation, and regular red-teaming. We never deploy an AI feature without a defined evaluation framework.
Initial build cost is similar to or slightly higher than traditional features. The key difference is ongoing inference cost, which scales with usage. We model your expected usage patterns upfront so you have a realistic cost projection before committing.