Where most AI projects stall
- Months of planning with nothing to show for it
- Models trained on clean lab data that fail on real inputs
- Vendor lock-in to platforms your team can't maintain
- Disconnected prototypes that never reach production
We hear these stories weekly from companies that spent their budget before writing a single line of inference code.
How we do it differently
- A working prototype in the first three weeks
- Training on your actual data, with all its noise
- Open-source tooling you own and can modify
- Deployment into your existing infrastructure, not ours
Every engagement ends with a handoff document and a two-hour walkthrough so your internal team can maintain the system independently.
What we build
Services tailored to your data and your deadlines
Each project starts from your specific bottleneck, not a generic feature list. Here are the categories most of our work falls into.
Predictive analytics
We build regression and classification models that forecast demand, flag anomalies, or score leads. Typical turnaround: four to six weeks from raw data to a live dashboard your sales or ops team uses every morning.
Natural language processing
From ticket classification to contract extraction, we fine-tune language models on your domain vocabulary. One logistics client reduced manual email triage from two hours a day to twelve minutes.
Computer vision
Quality inspection on a production line, document digitization, or inventory counting via security cameras. We handle labelling, training, and edge deployment so the model runs where the camera is, not in a distant data centre.
Custom API and integration
Your model is only useful if it connects to the systems your people already use. We wrap every deliverable in a documented REST API and, where needed, build connectors for Salesforce, SAP, or whatever ERP you run.
AI strategy and audit
Not sure where to start? We run a two-week audit: interview stakeholders, inventory your data assets, and deliver a ranked list of opportunities with estimated ROI and effort. No obligation to hire us for the build.
How it works
From first call to live system
Four phases, clear milestones, no surprises on the invoice.
Scoping call
A 45-minute video call where we map your pain point, identify the data you have, and agree on a success metric. If we think AI is the wrong tool, we say so.
Data and prototype
We ingest your data, clean it, and build a minimum viable model in two to three weeks. You see results on real inputs before committing to a full build.
Production build
We harden the model, write tests, set up monitoring, and deploy to your cloud or on-premise servers. This phase typically takes four to eight weeks.
Handoff and support
Documentation, a recorded walkthrough, and 60 days of bug-fix support. After that, optional retainer plans keep the model retrained as your data evolves.
Common questions
Things clients ask before signing
Let's figure out if AI fits your problem
Send us a short description of the challenge you are trying to solve. We will reply within one business day with an honest assessment of whether machine learning is the right approach and, if so, a rough scope.
