AI Development Services for Production Systems

Everyone has an agent demo. Almost nobody has an agent in production that they trust. We build tool-use agents using LangGraph state machines, MCP (Model Context Protocol) servers, and CrewAI multi-agent pipelines — with observability via LangSmith, human-in-the-loop checkpoints, and the kind of failure handling that turns a demo into a system you can actually operate.

Cursor and Copilot write code faster than manual QA can validate it. The flaky test problem gets worse as codebases grow. LLM features need eval harnesses, not just unit tests. We build AI-augmented QA infrastructure — AI-generated test suites, self-healing Playwright selectors, visual regression pipelines, and LLM evaluation harnesses — so your quality gates actually scale.

Most AI product failures are not engineering failures — they are strategy failures. The AI wrapper trap: you build a thin layer over GPT-4, your users love the demo, and then OpenAI ships the feature natively in ChatGPT. We help you find where AI creates durable advantage — proprietary data, workflow depth, network effects — not just capability you are renting from an API.

AI agents consume APIs as tools. Poorly described parameter names, inconsistent error responses, and undocumented edge cases cause agents to fail in ways that are hard to debug. We design APIs with OpenAPI 3.1 specifications and MCP-compatible tool schemas so your APIs work for both human developers and AI tool-calling architectures from day one.

AI inference is expensive when sized wrong. LLM serving on an oversized GPU instance that idles overnight charges proportional to allocation, not usage. vLLM and TGI changed the self-hosting calculus — the crossover point where self-hosting beats API pricing is lower than most teams think. We design cloud infrastructure for AI workloads: right-sized compute, MLOps pipeline infrastructure, and the cost governance that prevents the surprises.

YOLOv8 runs in real time on CPU-class hardware. Detectron2 segments with pixel-level accuracy. The models are not the hard part. The hard part is data distribution: a defect detection model trained on clean factory floor images fails on production images captured under shift-change lighting conditions. We build vision systems validated against your actual operating conditions, not a held-out split of the same dataset.

Training/serving skew is one of the most common production ML failures and one of the hardest to detect. It happens when feature computation at training time and serving time uses different logic — even subtly different NULL handling or timezone conversion. We build data pipelines with dbt transformations, Airflow or Prefect orchestration, and feature stores that make training/serving consistency structural rather than aspirational.

The "Cursor makes every developer 10x" narrative is real but incomplete. Cursor and Claude accelerate scaffolding and boilerplate. They do not solve AI-native UX patterns — streaming text rendering, agent state timelines, confidence indicators — that standard component libraries do not have. We build full-stack products where AI integration is designed in from day one, not retrofitted after launch.

MLOps maturity is the gap between a model that works in a notebook and a model that works in production six months after launch. Experiment tracking with W&B or MLflow. Model serving with vLLM, TGI, or FastAPI. The shift from training optimization to inference optimization — quantization, batching, KV cache tuning — that now dominates production ML work. We build the full stack.

On-device AI has matured. Apple Neural Engine handles transformer inference natively. TFLite and MediaPipe run at real-time frame rates on mid-range Android. The cloud/on-device split is now a genuine architecture decision: cloud for capability, on-device for latency and privacy. We build Flutter applications that make that split intelligently, feature by feature.

The post-transformer NLP landscape has two regimes: foundation models that handle complex reasoning and open-ended generation, and small language models (SLMs) fine-tuned for specific tasks that run faster and cheaper. Structured output and function calling have replaced most of what traditional NLP pipelines did with named entity recognition and intent classification. We build NLP systems that pick the right regime for each task.

Teams that launched AI products on OpenAI API calls are hitting unit economics walls at scale. The optimization surface is larger than most teams realize: semantic caching, model routing (cheap model for simple, expensive for complex), INT4/INT8 quantization, prompt caching on Anthropic and OpenAI, and the self-hosting crossover point where vLLM beats API pricing. We audit your AI spend and implement targeted reductions against verified quality baselines.

Prompt injection attacks, jailbreaking, indirect injection via retrieved documents, adversarial inputs to classifiers — the OWASP Top 10 for LLMs formalizes what practitioners have been discovering empirically. Agentic systems with tool access have a substantially larger attack surface than pure text generation. We run structured red team exercises against your AI systems and produce remediation plans grounded in actual exploits, not theoretical checklists.

Model quality is determined at annotation time, not training time. Ambiguous annotation guidelines produce inconsistent labels — and a model trained on inconsistent labels learns the annotator's uncertainty, not the underlying task. We design annotation processes with IAA measurement from the first batch, production-distribution coverage analysis, and RLHF preference data workflows for LLM fine-tuning.

The chatbot era is ending. Voice agents (ElevenLabs, PlayHT) with sub-500ms latency are viable for conversational products. Multimodal inputs — images, documents, voice — are now first-class in Claude and GPT-4o. The "uncanny valley" of AI conversations closes as personality design becomes a discipline. We build conversational AI systems designed for resolution rate, not just response coherence.

v0, Bolt, and Lovable have genuinely changed design-to-code velocity. They produce prototype-quality output in hours. What they produce is not production code: no accessibility semantics, hardcoded pixel widths, inline styles instead of design tokens, missing states. The vibe-coding revolution closed the designer-developer gap for demos. We close it for production.

The strangler fig pattern works for AI modernization. You do not need to replace a 20-year-old insurance claims system to add document AI to its intake workflow. An API facade captures all traffic. Document AI (AWS Textract, Azure Document Intelligence, custom extraction) wraps the paper-based processes. The legacy system continues handling what it does well while AI augments the workflows that benefit from it.

AI system due diligence has failure modes that general software due diligence misses. Model risk (claimed benchmarks vs. production performance on your inputs), data rights (training data provenance and licensing), vendor lock-in (what happens if OpenAI changes pricing or deprecates a model), and the demo vs. production gap — where a system performs impressively in a controlled demo and poorly on real user inputs. We test the system against your specific inputs before you close.

Cursor + Claude can build a working full-stack prototype in a weekend. What they produce is not production code: no authentication, no error handling, API keys committed to the repo, SQL injection via unparameterized queries, CORS open to all origins, no monitoring. The one-person startup is real. The prototype-to-production gap is also real. We bridge it.