Eight areas we build for operating businesses.

We build LLM-powered features for drafting, summarising, translating, code assistance, and creative production. The model produces a draft and a person signs it off before anything ships to a user.

Retrieval-augmented generation, prompt engineering, evaluation frameworks, caching, guardrails. Runs against your data in your cloud account. The weights, the prompts, and the eval sets all stay with you when we hand over.

Assistants, chatbots, and routing systems for customer support, internal queries, and operational triage. The hard part is the handoff: when the bot's confidence drops, the conversation moves to a human with the full history attached.

Multi-turn context, grounded retrieval, intent detection, and sentiment-aware routing. Wired into your CRM, helpdesk, or comms stack on the first sprint, because the integration is usually where these projects fail.

If a person on your team is keying invoice data into the ERP by hand, this is the service. Forms, invoices, contracts, statements, scanned PDFs: read, classified, and routed automatically, with a reviewer queue for the cases the model isn't sure about. The structured output lands directly in the ERP, CRM, or warehouse that consumes it.

OCR, layout-aware transformers, and small fine-tuned classifiers. Every prediction is logged with a hash of the source document and a confidence score, so you can reconstruct any decision the system made when an auditor asks six months later.

Cameras already running on your manufacturing line, retail floor, or logistics depot can do more than record. Object detection, image classification, visual quality inspection, OCR for everything that isn't a document, all running on the existing video feed.

Deployed at the edge or in your cloud account, depending on the latency you need. We evaluate on your actual production images. Vendor benchmarks tend to flatter the model, and lighting and camera angles in production will tell a different story almost every time.

Applied ML earns its keep here more often than anywhere else. Demand forecasts, pricing recommendations, revenue simulations, trend prediction. The workhorse of the field, and where we expect to focus a large share of our work.

Gradient-boosted demand models, time-series forecasting, elasticity estimation, counterfactual scenario analysis. Every forecast ships with prediction intervals (P10, P50, P90), so the downstream user knows how much to trust each number.

Models that decide which customers to retain, which to grow, and which to let go gracefully. Churn prediction, lifetime value, segmentation, propensity scoring. We build on top of the warehouse you already maintain, and avoid standing up a parallel data store that the consultancy ends up owning.

The output goes where your team already works: a ranked list in the CRM, a flag on the account record, a Slack alert when something material changes. A model that lives in a Jupyter notebook nobody opens is a model that doesn't deliver value.

The infrastructure that decides whether your models survive contact with a Tuesday morning. Drift detection, automated retraining, A/B testing, model registries, rollback procedures. The unglamorous parts most projects skip in week one and end up writing a postmortem about in month nine.

We harden existing models for production, or design the platform before the first model is trained, depending on what stage you're already at.

Production websites and web applications, shipped on short cycles. AI handles the dull parts (copy drafts, image selection, SEO scaffolding, accessibility audits). Design direction and judgement calls stay with a person.

Next.js by default and Vercel-friendly. Your code lives in your repository from day one and has no hidden dependencies on platforms we'd prefer to lock you into.