73% of enterprise AI projects never reach production.
The reason is almost never the model - it's pipelines built for reporting being asked to feed real-time decisions. We fix that layer without replacing your stack.
Abhishek - Founder, DataTheta
Enterprise Data & AI Architecture · 20+ yrs
"Every stalled AI programme I've reviewed had a brilliant team and a data foundation built for dashboards, not decisions. That's a fixable architecture problem - not a talent one."
40+
AI systems in production
8 wks
To first live outcome
68%
Avg. remediation time cut
4
Regulated industries served
Where enterprise AI actually breaks.
Most teams optimise the wrong layer. We work through the stack from the bottom up – fixing the foundation before tuning the model.
- L3 & L2 failures propagate silently upward - causing AI errors that look like model problems but aren't.
- Fix L1 first and the failures at L2, L3, L4, L5 resolve without rip-and-replace.
- // SELF-CHECK
Five signs your data foundation is blocking AI.
If three or more of these resonate, your AI programme is stalled at the foundation – not the model. It’s a solvable architecture problem. We fix it without rip-and-replace.
- 01
Your models are accurate in testing but wrong in production
Feature drift between training and serving environments - caused by pipeline inconsistency, not model quality.
- 02
Every sprint, 40–60% of effort goes into data fixes
Engineers spend more time remediating upstream issues than building. A symptom of brittle ingestion architecture.
- 03
AI outputs can't be explained or traced to source
Decisions are being made on outputs with no lineage. Regulators, auditors, and the board will ask - and you won't have an answer.
- 04
You're hiring senior data engineers but still slipping
Talent isn't the bottleneck. The average time-to-hire is 87 days - and new engineers inherit the same broken foundations.
- 05
Dashboards and AI draw from different versions of the truth
Multiple copies of 'the data' means your teams are making decisions based on different realities - and nobody can prove which is right.
We fix this without rip-and-replace. We fix it at the source – so your existing stack, team, and tools become capable of supporting production AI.
The alternative, in production.
Not a new tool. A set of decisions that make your data layer capable of supporting real-time AI – without replacing the stack you’ve already built.
8 wks
Average time from engagement start to first production outcome. Not a roadmap – a live system.
- 01
Streaming-first pipeline architecture
A focused 12–24 month plan that prioritizes AI use cases, business value, feasibility, risks and execution milestones.
- 02
Data contracts between producers and consumers
Schema agreements enforced at the boundary - not discovered when a model silently breaks. Every upstream change is negotiated, not assumed.
- 03
MLOps embedded in the pipeline, not bolted on
Feature stores, model registries, and serving infrastructure built into the data layer from day one - not retrofitted after the model is live.
- 04
Lineage and observability across the full stack
End-to-end traceability from raw source to model output. When something drifts, you know what changed, when, and why - in minutes, not days.
- // CAPABILITIES
Six service lines. One accountable partner.
From raw data to live AI – we own every layer of the stack, so accountability never falls between vendors.
- Foundation & Strategy
Data Foundation & Advisory
Strategy, architecture, and governance before you build. AI readiness assessed against real criteria.
- Infrastructure & Pipelines
Data Engineering
Context-aware pipelines, streaming, warehouse and lakehouse architecture, DataOps.
- Artificial Intelligence
AI & GenAI
RAG systems, agentic architecture, copilots - trained on your data, deployed in your environment.
- Predictive & Advanced Analytics
Data Science & ML
Predictive models, NLP, computer vision - production-ready from the first sprint, with MLOps.
- Business Intelligence
BI & Analytics
Decision intelligence and self-serve analytics your teams actually use - no engineering queue.
- Flexible Talent
On-Demand Experts
Senior engineers embedded on an FTE model. Two weeks to placement, not 87 days.
Leaders I've met with recently.
I have candid, no-script conversations with senior leaders every week - about what's shipping, what's stalled, and where data and AI genuinely move the needle.
The pattern I see is almost always the same.
- 20+ years in enterprise data and AI architecture
- 40+ production AI systems delivered across 4 regulated industries
- Clients include pharma, manufacturing, logistics, and financial services leaders
- No offshore handoff - senior engineers only, accountable from day one
Abhishek
Founder & CEO - DataTheta
I've been called in to rescue enough migrations to know how they go wrong.
By the time a CIO calls me, it's usually one of two situations. Either they're about to commit a very large budget and something in their gut says the plan is too clean. Or the programme is already eighteen months in, twice over budget, and the board is asking questions.
In both cases the cause is nearly identical. Nobody spent enough time on discovery. The legacy logic was more tangled than anyone admitted. The design was lifted rather than rethought. Governance was left for later. And by the time that surfaced, it was expensive.
That's why DataTheta doesn't start with a platform recommendation. We start by telling you what's actually in your estate - and occasionally, we tell CIOs that they shouldn't migrate at all yet; that a federated layer gets them to AI-readiness faster and cheaper. That's a smaller engagement for us. We say it anyway, because being right matters more than being hired.
What leaders say about working with DataTheta
Enterprise teams trust DataTheta to turn complex data challenges into production-ready AI, analytics, and decision systems.