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AI systems that work reliably — not just in a demo, but in production
We build production AI systems: retrieval-augmented chatbots grounded in your own data, workflow automation, self-hosted inference for cost and privacy control, dataset curation, model fine-tuning, and evaluation pipelines that catch regressions before your users do. You get a system where every change is tested and the cost per useful answer is a number you can track.
What we build
The gap between an AI demo and an AI product is evaluation. A demo works on five hand-picked examples. A product works on the sixth, the sixty-first, and the one with a typo in it — and when it fails, you know within minutes rather than after a customer complains. We build AI systems where the evaluation is as carefully designed as the model itself. Real outcomes: a retrieval-augmented support assistant grounded in hundreds of thousands of documents with citations and a verified factuality rate measured on a held-out test set, an automated workflow that classifies and routes thousands of inbound requests per day with per-category precision tracked on a dashboard, and a self-hosted language model serving high-throughput inference with strict data isolation between tenants. We use managed AI APIs (Anthropic, OpenAI, Google) when operational simplicity and latency matter most, and self-hosted open models when data residency, cost at scale, or fine-tuning requirements make the economics work. The principle: every prompt change passes a written evaluation, every model upgrade replays it, and the cost of a useful answer is always tracked.
Capabilities
Retrieval-augmented generation — AI answers grounded in your own documents and data, with source citations, hybrid search across your knowledge base, and result re-ranking for accuracy.
Workflow automation — AI-powered automation pipelines that classify, route, transform, and act on data, with full audit trails and human escalation paths for edge cases.
Self-hosted inference — open-weight language models running in your infrastructure with GPU acceleration, paged memory management, and multi-tenant isolation for cost control and data privacy.
Model fine-tuning — adapting a foundation model to your domain and terminology using efficient techniques that preserve the base model's general capability while improving on your specific task.
Dataset curation — cleaning, deduplicating, and labelling training data with PII removed, quality filters applied, and an active-learning loop to direct annotation effort where it matters most.
Evaluation pipelines — task-specific test suites that run automatically on every change, measuring accuracy, groundedness, helpfulness, and safety across multiple axes rather than a single number.
Guardrails — input and output filters for harmful content, PII, and prompt injection, with sandboxed tool access and structured prompts that prevent instruction manipulation.
Cost and observability — per-request cost tracking, token-level traces so you can see exactly what the model received and returned, and drift monitoring that alerts when output quality degrades.
Inference: vLLM, TGI, Ollama, OpenLLM, Triton Inference Server for non-language workloads
Orchestration: n8n for workflow automation, Temporal for long-running jobs, LangGraph for stateful agents, Ray for distributed batch processing
Retrieval and storage: PostgreSQL with vector extension, Qdrant, OpenSearch, Redis, S3 for raw document storage
Training: PyTorch, Hugging Face Transformers and PEFT, Axolotl, DeepSpeed, Modal or AWS SageMaker for managed training runs
Evaluation and observability: RAGAS for retrieval quality, lm-eval-harness for benchmarks, Phoenix, Langfuse, Weights and Biases, custom golden test sets per task
How we work
Problem definition and evaluation design — we write the task definition, build a representative test set with expected outputs, and define the metrics before touching a single model. This prevents the most common failure: discovering three months in that you have been optimising the wrong thing.
Baseline comparison — a managed-API baseline, an open-weight baseline, and a retrieval-only baseline measured against the test set. We publish the results so the platform choice is evidence-based.
Production build — the chosen architecture built with guardrails, observability, and cost tracking in place from day one — not added later when something goes wrong.
Evaluation-gated rollout — live traffic split between the new system and the baseline, promoted to full traffic only when accuracy, latency, and cost per resolved task all improve or hold.
Operate and re-evaluate — drift monitors, a scheduled re-evaluation cadence, and a written plan for updating prompts and models as the landscape improves.
Where this fits
AI and ML engineering returns the most value in support automation, internal knowledge retrieval, contract and document analysis, clinical triage, financial document processing, and operations teams dealing with high volumes of semi-structured incoming work. We most often get called in when a proof-of-concept demo works in a presentation but fails on real data, or when a vendor chatbot is producing answers that are wrong in ways visible to customers.
What you get
Written task definition, success metrics, and a versioned test set with rationale documented per example.
Production deployment with cost dashboards, drift monitors, and per-request traces from input to output.
Evaluation pipeline runnable in CI on every prompt or model change, with pass/fail gates.
Fine-tuning configuration, dataset documentation, and a model card per released version.
Runbook covering hallucination triage, prompt injection response, and a model deprecation plan.
FAQ
How do you measure whether the AI is actually working?
We build task-specific test sets scored on multiple dimensions: factual accuracy, groundedness in source material, helpfulness, and safety. A single accuracy number hides the failure modes that matter — a system can score 90% accuracy while being wrong on exactly the sensitive cases your users care about. Once live, we also track user-facing signals: resolution rate, escalation rate, and explicit feedback, and correlate them with the automated scores.
When should we use a managed API versus hosting our own model?
Managed APIs first. The operational cost of running open-weight models well — GPU infrastructure, scheduler tuning, monitoring, on-call — is real and most projects do not justify it on day one. We move to self-hosted models when data residency regulations prevent sending data to a third party, when sustained throughput at scale makes the economics work, or when domain-specific fine-tuning requires it. We revisit this recommendation every quarter as both costs and model capabilities change.
How do you prevent the AI from being manipulated through user input?
We treat the AI model like untrusted code running in a sandbox. User input is separated from system instructions using structured templates, retrieved context is labelled so the model knows its source, tool calls require explicit user intent before execution, and every output passes through a classifier before any side effect occurs. We test the system against known attack patterns before launch.
Can you fine-tune on our sensitive data securely?
Yes. Training runs inside your cloud environment or a dedicated tenant on a managed training platform, with PII removed from the dataset before training begins. The resulting model weights stay within your account and are never shared. We prefer parameter-efficient fine-tuning adapters over full model retraining where possible — this keeps the base model capability intact while adapting to your domain, and produces a smaller, auditable change.
Ready to start?
Send us a 200-word description of the task, two example inputs with your ideal outputs, and the constraint that hurts most — cost, latency, accuracy, or data residency. You will receive a baseline plan and a fixed-price discovery quote within three business days.
