← ALL SERVICES · 04
AI & machine learning
Custom models, LLM integrations and RAG pipelines that hold up in production — built by three engineers who measure retrieval accuracy before anyone talks about the demo.
LANGCHAIN · FASTAPI · PYTORCH · TENSORFLOW · VECTOR DBS · N8N
WHAT WE BUILD
LLM integrations
Chat, extraction and summarisation wired into your product behind FastAPI — with prompt versioning, fallbacks and cost ceilings, so a model change is a config change, not a rewrite.
RAG systems
Retrieval pipelines over your documents — chunking, embeddings, vector search and reranking tuned against an evaluation set, not eyeballed until the answers look plausible.
Custom-trained models
Document classifiers, predictive-maintenance models and other supervised builds in TensorFlow or PyTorch — trained on your data, benchmarked against the baseline they have to beat.
Workflow automation
Multi-step automations in n8n and LangChain — document routing, approvals, back-office chores — with human checkpoints exactly where a wrong answer would cost you.
Marketing & content automation
n8n pipelines that draft, schedule and publish — the same stack that runs our own marketing and YouTube channel — with human review before anything ships.
MLOps & serving
Versioned models behind monitored FastAPI endpoints — latency budgets, drift alerts and rollback paths, because a model nobody can redeploy is a liability.
HOW IT RUNS
01
Feasibility pass
A short spike against your actual data — we tell you whether this needs an LLM, a classifier or a cron job.
02
Evaluation set first
Before any pipeline exists, we build the test set that defines correct — every later choice gets measured against it.
03
Build and tune
Model, retrieval and prompts iterated against the eval set — chunking strategies, rerankers and model swaps judged by score, not vibes.
04
Ship and monitor
Deployed behind FastAPI with logging on every call — we watch accuracy, latency and cost in production and hand over the runbook.
STRAIGHT ANSWERS
How do you decide which model to use?
We benchmark candidates against your evaluation set and your budget — a hosted LLM where accuracy and speed justify the per-call cost, a fine-tuned or custom model where volume, latency or data privacy argue for owning the weights. The choice is a measured trade-off, written down, and revisited when models change.
What does an AI project cost?
A fixed-price feasibility spike first, so you're not funding an open-ended research project. After that, scoped builds are quoted per phase; inference costs are modelled up front and reported monthly — the API bill should never be a surprise.
Who owns the models and IP?
You do. Code, prompts, fine-tuned weights, evaluation sets and vector indexes are delivered to your repositories and cloud accounts under a work-for-hire contract. We keep nothing that would make leaving us expensive — the runbook is written so your team can operate it without us.
What happens after launch?
Models drift and providers deprecate endpoints, so we offer a monthly retainer — monitoring accuracy and cost, re-running evals when models update, and patching pipelines when APIs change. If you'd rather run it yourself, the handover includes dashboards, alerts and the eval harness.