AI that ships, not AI that demos
We engineer production-grade AI systems for teams that have outgrown demos. Real evaluation infrastructure, real cost models, real human-in-the-loop guardrails — built around Claude, OpenAI, MCP, and the agent patterns that actually work in 2026.
The gap between an AI demo and a production AI system is wider than most teams expect
A working prototype is now a weekend project. A reliable production system that handles real users, real edge cases, real cost economics, and real compliance is still a serious engineering build. We focus on the production engineering layer: tool architecture, eval infrastructure, cost and latency budgets, observability, and the human-in-the-loop boundaries that turn a clever model into a system the business can actually rely on.
- Production AI Agent Engineering
- MCP Server Development & Integration
- Retrieval-Augmented Generation (RAG) Systems
- LLM Fine-Tuning & Distillation
- Multimodal AI (Vision + Text + Voice)
- Predictive Analytics & Forecasting
- Agent Evaluation Infrastructure
- AI Cost & Latency Optimization
- Prompt-Injection Defense & AI Security
- Computer Vision Pipelines
Production AI Agents
Multi-turn agents with explicit risk tiering, scoped tool permissions, idempotency on state-mutating calls, and human approval gates on consequential actions. Architected from day one for observability and rollback.
MCP Server Engineering
Model Context Protocol servers that expose your internal systems — CRM, ticketing, databases, internal APIs — to any compliant AI client. Authenticated at the boundary, scoped per tool, versioned independently.
Grounded Conversational AI
Multi-turn assistants with RAG grounding, citation-backed outputs, prompt-injection filtering, and structured escalation to human agents. Not chatbots — purposeful copilots inside your existing workflows.
Everything you need to succeed
Production AI Agents
Multi-turn agents with explicit risk tiering, scoped tool permissions, idempotency on state-mutating calls, and human approval gates on consequential actions. Architected from day one for observability and rollback.
MCP Server Engineering
Model Context Protocol servers that expose your internal systems — CRM, ticketing, databases, internal APIs — to any compliant AI client. Authenticated at the boundary, scoped per tool, versioned independently.
Grounded Conversational AI
Multi-turn assistants with RAG grounding, citation-backed outputs, prompt-injection filtering, and structured escalation to human agents. Not chatbots — purposeful copilots inside your existing workflows.
Custom Model Fine-Tuning
Fine-tune Claude, Llama, Mistral or open-source models on your proprietary data — when the cost-quality math actually justifies it over prompting. We will tell you when it does not.
Eval Infrastructure That Catches Regressions
Golden sets, synthetic adversarial test suites, production replay against candidate prompts. Run on every prompt change and model upgrade. The discipline that separates AI features that improve from ones that drift.
Computer Vision in Production
Object detection, OCR, defect detection, video analytics — engineered for real-world inference cost and latency, not benchmark accuracy alone. YOLOv8/v11, SAM, custom CNNs, edge-deployable variants.
Predictive Analytics That Actually Predict
Time-series forecasting, demand prediction, churn modeling, anomaly detection. Built with proper train/test/validation splits, backtesting on real historical data, and confidence intervals you can show stakeholders.
Cost & Latency Optimization
Two-model architectures (fast triage + reasoning), aggressive caching, prompt-prefix caching, deterministic fallbacks for routine paths. Most clients see 70-90% cost reduction without quality loss.
AI Security & Governance
Prompt-injection defenses, secrets isolated from agent context, MCP per-tool permissions, audit logs on every action, model explainability for regulated workflows. The guardrails that make AI features pass legal review.
AI Integration Into Existing Stacks
Most clients do not need a new AI product — they need AI augmentation of what they already have. Document intelligence, smart search, predictive features added to your existing platform without a rebuild.
How we build with you
Architecture Decision Up Front
Reactive, conversational, or autonomous? The choice shapes everything downstream. We pick on purpose, not by accident, and document the trade-offs so future decisions stay coherent.
Spec & Eval Set Before Any Code
A short, testable specification and a hand-curated evaluation set come before any prompt or tool is written. The eval set is the contract — it tells us when we are done and catches regressions forever.
Tool Layer First, Model Second
Tools are 80% of the engineering. Idempotent state mutations, scoped permissions, structured error responses, full audit logs. The model gets connected last, to an interface that already works.
Production Hardening & Observability
Per-turn structured logging, distributed tracing across model + tool + external API calls, dashboards on loop length, tool-call success rate, and cost per successful task. You ship knowing what to watch.
Continuous Eval & Cost Review
Monthly model upgrades run through the eval suite before promotion. Cost dashboards reviewed quarterly. Prompt and architecture changes versioned in git. The AI feature gets better with age instead of drifting.
Built with proven technologies
Common questions
A short feasibility engagement (typically 1-2 weeks) answers this honestly. We have walked clients away from AI builds when a deterministic rules engine or a well-indexed search would solve the same problem at a fraction of the cost and complexity. The good answer to "should we use AI here" is sometimes "no, here is what to use instead".
Model Context Protocol is the open standard Anthropic released in late 2024 for connecting AI models to internal systems. By 2026 it has become the default integration pattern for production agents — a properly-built MCP server is portable across AI clients (Claude, OpenAI, others) and survives model upgrades. Building MCP-first means your AI investment is not locked to one vendor.
Three layers: (1) RAG grounding so model outputs are tied to retrieved sources with citations, (2) input filtering and output validation, with structured schemas the model output must conform to, (3) confidence scoring with escalation to human review for high-stakes outputs. Plus continuous eval against an adversarial test set that grows over time.
Two-model architectures are the default — a fast cheap model (Haiku, GPT-4o-mini) handles triage and routine turns, a larger model (Sonnet, Opus, GPT-4o) handles hard reasoning. Add aggressive prompt-prefix caching, tool result caching, and deterministic fallbacks for routine paths. Most production agents we ship are unit-economically positive within the first month of operation.
Usually no. Foundation models trained in 2025-2026 are capable enough that most enterprise use cases work with zero or few-shot prompting plus RAG grounding on your existing documents. Fine-tuning becomes valuable when you need very specific output formats, very domain-specific vocabulary, or significantly lower inference cost at high volume — we will tell you which case you are in honestly.
That is the majority of what we ship. Smart search over your existing knowledge base, document intelligence on top of your current data pipeline, predictive features added to your existing dashboards, copilots embedded in your current product — all without disturbing the underlying system. Most AI value capture in 2026 is augmentation, not greenfield builds.
A golden evaluation set of 50-200 hand-curated input/output pairs is the foundation, run on every prompt or model change. Production replay samples a small percentage of real user traffic and runs candidate changes against it offline. Human-flagged outputs from real users feed back into the golden set over time. The result is a steady, measurable trajectory of quality — not a vibes-based "looks better to me".
Architected correctly, you can. We build provider-agnostic interfaces — the application code talks to an abstraction that can swap between Claude, OpenAI, or open-source models. MCP servers are portable by design. Prompts get version-controlled per model so swaps are tested, not surprises. Vendor lock-in in AI is mostly an architecture failure, not a contractual one.
Ready to get started?
Let's discuss your project and see how we can help you build something extraordinary.