From AI pilot to production.
Most firms can build an AI demo. We put AI into production, grounded on your data, integrated through robust APIs, and run securely on the cloud. RAG, agents, and copilots engineered for measurable ROI, on the same disciplined foundation as our Architecture, API, and Cloud practices.
Applied AI, end to end
From strategy to production and operations: the full path to AI that actually ships, grounded on your data and integrated with your systems.
AI Solution Architecture
Turn AI ambition into an actionable plan. We assess use cases, weigh build-vs-buy, and design a secure, cost-aware reference architecture grounded in your data.
- AI readiness & use-case assessment: shortlist the highest-ROI opportunities.
- Build-vs-buy & model selection: proprietary vs open (Claude, GPT, Gemini, Llama, Mistral).
- Reference architectures: RAG, agents, and data flows designed to scale.
- Data strategy: pipelines, embeddings, and governance for grounding.
- Security & cost modeling: guardrails and token economics planned up front.
- Phased roadmap: from pilot to production with clear milestones.
AI Engineering: RAG & Agents
The build core. We engineer production-grade RAG pipelines and agentic workflows that are measurable, grounded, and integrated with your systems.
- RAG pipelines: chunking, embeddings, vector stores, and retrieval tuning.
- Agentic workflows: tool use and orchestration (LangGraph, CrewAI).
- MCP servers & tools: connect AI to your data and systems via Model Context Protocol.
- Prompt engineering & fine-tuning: the right technique for each use case.
- Evaluation harnesses: measure accuracy, quality, and regressions (Ragas, LangSmith).
- Grounding & accuracy: reduce hallucinations with retrieval and guardrails.
AI-Assisted Application Development
Embed AI into your products, and accelerate delivery with AI-assisted engineering.
- Copilots & assistants: domain-specific helpers inside your apps.
- Semantic search: natural-language search over your content.
- Document intelligence: extraction, summarization, and classification.
- Conversational interfaces: chat and voice grounded on your data.
- AI-accelerated delivery: Copilot, Cursor, and Claude Code to ship faster.
- Legacy modernization: AI-assisted understanding and refactoring.
AI Gateway & Integration
AI is only as useful as what it can reach. We govern LLM traffic and connect AI to your enterprise, built on our deep API expertise.
- AI gateways: govern, secure, and observe LLM traffic (Apigee, Kong AI Gateway, Azure APIM).
- Cost & rate control: budgets, quotas, and caching to tame token spend.
- AI-as-API: expose models and agents as managed, versioned APIs.
- Tool & MCP endpoints: the APIs your agents call, engineered right.
- Identity & access: authentication, authorization, and data-scoping for AI.
- System integration: wire AI into existing apps, data, and workflows.
LLMOps & AI on Cloud
Run AI reliably and cost-effectively in production on AWS, Azure, and Google Cloud.
- Platform deployment: AWS Bedrock/SageMaker, Azure OpenAI/AI Foundry, Google Vertex AI.
- Scaling & reliability: production-grade, resilient AI infrastructure.
- Observability & evals: monitor quality, latency, and drift in production.
- Guardrails & safety: content filtering and policy enforcement at runtime.
- AI cost optimization (FinOps): right-size models and control token spend.
- CI/CD for AI: automated, repeatable deployment of models and prompts.
The modern AI stack, applied with discipline
We're model- and platform-agnostic. We choose what fits your goals, data, and budget.
Models & Frameworks
- Claude, GPT, Gemini, Llama, Mistral
- LangChain / LangGraph
- LlamaIndex, Semantic Kernel
- CrewAI agent orchestration
Cloud AI & Retrieval
- AWS Bedrock & SageMaker
- Azure OpenAI / AI Foundry
- Google Vertex AI
- Pinecone, Weaviate, pgvector, Azure AI Search
Integration & Ops
- Model Context Protocol (MCP)
- Apigee, Kong AI Gateway, Azure APIM
- Ragas, LangSmith, Langfuse evals
- GitHub Copilot, Cursor, Claude Code
A clear path from idea to production
A pragmatic, low-risk methodology that gets AI past the pilot stage and into reliable production.
Assess
Use cases, data readiness, and build-vs-buy.
Design
Reference architecture, data strategy, and roadmap.
Build
RAG, agents, integrations, and MCP tooling.
Evaluate
Measure quality, safety, and cost with real evals.
Operate
Deploy, monitor, and optimize with LLMOps.
Governance, security & evaluation, from day one
Responsible AI isn't a bolt-on. We engineer trust into every solution so it's safe, accurate, and audit-ready, essential for regulated, high-stakes environments.
Guardrails & safety
Content filtering, policy enforcement, and red-teaming to keep AI on the rails.
Evaluation & accuracy
Measurable quality with eval harnesses. We prove it works, not just claim it.
Security & data privacy
Data-scoping, access control, and protection of sensitive information across the stack.
Responsible & compliant
Practices aligned to emerging standards (EU AI Act, NIST AI RMF) and your policies.
Ready to move from AI pilot to production?
Get a free AI Readiness Assessment: a clear, honest plan with no obligation. We respond within one business day.