Data Equity has five platforms with AI running in production. That taught us where a language model earns its place — and the far longer list of places it must never sit. We bring that judgement to startups finding product-market fit and to NBFCs, insurers and enterprises who have to answer to a regulator.
Five practices. Most engagements combine two or three of them.
You need a first credible AI feature, not a research programme. We map the opportunity, pick the one use case that changes your product, design an architecture a small team can actually operate, and build the evaluation harness that tells you whether it is working.
You need the AI conversation and the audit conversation to be the same conversation. We work on document intelligence, origination and servicing workflow, enterprise assistants over internal knowledge, model governance, and the evidence trail that makes a regulator meeting short.
Co-pilot, not autopilot. It removes the tedium from expert work; it never becomes the expert. Where a model must act, it carries an explicit autonomy ceiling and abstains below a confidence threshold rather than guessing.
Client names are withheld unless a reference has been agreed. These are the shapes of engagement we are repeatedly asked for.
Development, feature enhancement, system integration, L3 defect resolution and ongoing maintenance of an internal AI assistant serving business users — delivered as a dedicated offshore engagement with transparent tracking and mutually agreed estimates.
Replacing spreadsheet and document-specification calculation logic with a governed, versioned, API-enabled engine — including approval workflow design, rate-table migration and the reproducibility story for historical recalculation.
Opportunity mapping, use-case selection, architecture, prompt and cost governance, evaluation harness and production hardening — sized so a small in-house team can own it afterwards.
Options can be selected individually or in combination. Commercials are agreed per engagement; scope, commitment period and resourcing follow the option chosen.
A fixed monthly arrangement providing an on-demand pool of offshore development hours. Capacity is reserved for you for the full month; each task's effort is mutually agreed in writing before execution, and work beyond the agreed pool is quoted separately.
Dedicated Level-3 support on an annual term — diagnosis and resolution of production defects, root-cause analysis and bug fixes, invoiced monthly in arrears, with a defined notice period after the minimum commitment.
One full-time offshore developer allocated to your AI workstream, on a rolling monthly basis with a short minimum commitment and monthly extensions by mutual agreement.
A senior architect working onsite or offshore, covering solution architecture, technical direction and hands-on development — for programmes where the design decisions matter more than the headcount.
A defined scope, milestones and acceptance criteria, delivered to a fixed plan — suitable when the outcome is well understood at the outset.
We take a product from concept brief through architecture, build, launch and support — the same way we built our own platforms. Suitable for founders and business units without an in-house engineering function.
Delivery. Work is delivered by our offshore team unless an onsite presence is agreed, tracked and reported transparently throughout the engagement, and covered by confidentiality and data-security terms appropriate to regulated environments.
We map two or three of your current pain points against what is genuinely achievable — and say plainly which ones AI should not touch.
A written assessment: use case, data readiness, architecture, guardrails, evaluation approach, cost envelope and the risks worth naming.
One narrow slice built end to end and run in parallel with the existing process, with measurable acceptance criteria agreed before we start.
Quotas, caching, PII handling, audit logging, monitoring, runbook and capacity sizing — the work that separates a demo from a system.
Either your team takes it, with documentation and knowledge transfer, or we continue under a support or dedicated-resource model.
Tell us the process, the document pile or the decision you want to improve. The first conversation is about whether it is worth doing at all.