Most machine learning work stalls in a notebook. We design, build and operate the systems that don't — predictive models, retrieval assistants, and the MLOps pipelines that keep them running after launch.
Each engagement ends with something running in your environment — not a slide deck.
Forecasting for demand, churn, risk and capacity — built on your historical data and validated against real outcomes, not synthetic benchmarks.
The reporting layer underneath good decisions: modelled warehouses, governed metrics, and dashboards people actually open twice.
Autonomous workflows that take real actions in your systems, with tool access, guardrails and a human checkpoint where the stakes justify one.
Question answering over your private documents — contracts, SOPs, tickets, research — with citations back to source so answers can be checked.
Custom models for problems off-the-shelf tools don't cover — computer vision, NLP, ranking and recommendation, scoped to a measurable target.
The part most teams skip: versioned data, reproducible training, automated deployment, and drift monitoring that pages someone when accuracy slips.
You can stop after any stage and keep everything produced up to that point.
We audit your data, agree the metric that defines success, and say plainly whether ML is the right tool. Sometimes it isn't.
A working model against your real data, benchmarked on a simple baseline. If it can't beat the baseline, you find out in weeks, not quarters.
Deployment into your cloud, integrated with your systems, with the pipelines and tests needed to retrain it safely.
Monitoring for drift and degradation, scheduled retraining, and a handover so your team can run it without us.
Domain context changes the model. These are the sectors we know well enough to challenge your assumptions.
Predictive maintenance, yield optimisation and visual quality inspection on the line.
Vision · ForecastingCredit risk scoring, fraud detection and document extraction across underwriting workflows.
Risk · NLPClinical analytics, medical imaging support and operational forecasting for capacity planning.
Imaging · AnalyticsContent classification, recommendation systems and audience analytics at catalogue scale.
Ranking · RecsysThis is the section that decides whether an enquiry arrives. It needs your real numbers.
Client or sector · the problem in one line · the measurable result (percentage, hours saved, cost avoided) · the stack used.
Client or sector · the problem in one line · the measurable result · the stack used.
A direct quote, with the person's name, role and company. One real quote outperforms three anonymous ones.
I left these empty on purpose. I don't have your delivery history, and invented client names or results on a live services page are a real liability — legally and with any buyer who checks. Send me the details and I'll write them in.
Discovery engagements are fixed-price and scoped in days. Build projects are quoted after discovery, because an accurate number depends on the state of your data — quoting before we've seen it would be guesswork. We'll give you a range in the first call.
A prototype against your real data usually lands in four to six weeks. Production deployment depends on your infrastructure and approval process, and we scope that separately once the prototype proves the approach.
Often, yes. We can lead delivery, embed alongside your engineers, or act purely as a reviewer on work already underway. Handover and documentation are part of every engagement either way.
You do. All code, models, and documentation are delivered into your repositories and your cloud account. There is no licensing arrangement that keeps you tied to us.
We work inside your environment wherever possible. Where data must move, it's covered by an NDA and a written processing agreement before anything is transferred.
A short brief is enough to start. We'll reply within one working day with either a scoping call or an honest referral elsewhere.
Send a brief
Tell us the problem, the data you already hold, and any deadline you are working to. That is enough for us to come back with a scoping call or an honest referral elsewhere.
Email your briefPrefer webmail? Copy this address: abhishek@dataspoof.in