The team that builds our products is available for yours.
Data engineering, data science and AI consulting from the engineers who built the analytics layer inside Vistaar EHS. We do the work we already do on our own products, on yours.
Four capabilities, one team.
Data Engineering
Pipelines, warehouses and streaming infrastructure. Cleaning, enrichment, transformation and aggregation at scale — built so your team can maintain it after we leave, with documentation and a runbook rather than tribal knowledge.
Typical problems: data spread across systems that disagree; reporting that takes a week; a warehouse nobody trusts.
Data Science & Machine Learning
Forecasting, classification, anomaly detection and sentiment analysis — taken from notebook to a production service with monitoring, drift detection and a retraining plan. A model that is not deployed is not a result.
Typical problems: demand forecasting; predictive maintenance; risk scoring; quality defect detection.
AI & GenAI Consulting
An honest read on where a language model genuinely helps, where it does not, what it will cost to run at your volume, and what it will do to your data governance. Sometimes the answer is a rules engine, and we will say so.
Typical problems: document extraction; internal knowledge search; support triage; report drafting.
Cloud & Data Platform
The infrastructure underneath all of the above — environments, orchestration, access control, cost management and monitoring. Including on-premise, because plenty of manufacturing data is not allowed to leave the building.
Typical problems: cloud costs nobody can explain; no staging environment; access managed by spreadsheet.
The risk model inside Vistaar EHS.
Eighteen months of near-miss and incident data from multi-site operations, turned into a 30-day forward risk index per plant area. Built by this team, running in production, retrained weekly and monitored for drift.
It is the most useful thing we can show you: not a case study we cannot name, but a live system you can go and look at. If we can do it to our own product, we can do it to your data.
Four ways to start, depending on how much certainty you have.
Nobody should sign a six-month contract for a problem neither side has scoped. Most engagements start at the top of this list and move down only if the answer is worth it.
Feasibility spike
Two to three weeks. Is this solvable with the data you actually have? You get a written answer either way, including "no".
Fixed feeProof of concept
Four to eight weeks against a success metric agreed in writing before we start.
Fixed feeProject delivery
Defined scope, handed over with documentation, tests and a runbook your team can operate.
Fixed bidEmbedded team
Engineers working inside your team, your tooling and your sprint cadence.
MonthlyFrom first call to handover.
What we work with
We do not arrive with a platform to sell you. This is what we have shipped on — if your organisation has standardised elsewhere, that is usually where the work should live.
Will you work with our data if it cannot leave our network?
Yes. A significant share of manufacturing and pharma data is not permitted to leave the building, and we build for on-premise deployment on the product side already. The same applies to consulting engagements.
What if our data isn't good enough?
That is what the feasibility spike is for. It is a common, honest outcome — and knowing it in two weeks for a fixed fee is far cheaper than discovering it in month five of a build.
Do you hand over the code?
Yes. On project and proof-of-concept work you own the output, including the code, the documentation and the runbook. We are not trying to make you dependent on us for maintenance.
How is this different from hiring a data science agency?
We run production software ourselves. The people who would work on your problem also carry a pager for a live product used by safety teams. That tends to change what gets built.
Can we start small?
Yes — a two to three week feasibility spike is the intended front door, and most engagements start there.