For your role
For CDOs
What you are trying to solve
Business teams want to move fast. Engineering wants clean APIs. Compliance wants audit trails. Your team is managing a lakehouse, a warehouse, a dozen pipelines and a backlog of quality issues that predates the current AI wave.
The pilots that fail in your world fail for predictable reasons: the retrieval corpus was never curated, lineage stops at the export, feature definitions drift between training and serving, or residency rules were interpreted differently by legal and engineering.
What we deliver for you
- A data readiness assessment as part of discovery — sources, quality, access, lineage and residency for the chosen use case
- Lakehouse or warehouse configuration fit for AI workloads, with ingestion, transformation and quality monitoring
- Feature and vector stores where the workload needs them, with clear ownership and refresh schedules
- Access control, lineage and residency controls aligned to your region
- An evaluation harness that tests models and agents against your data, not a public benchmark
- Documentation that your team can maintain after handover
How we approach data for AI
We do not treat data as a one-time migration. For every production AI system, we define what data the model or agent reads, how often it refreshes, who approves changes and how quality is monitored. That is the same discipline as any analytics product — with higher stakes because the model acts on what it retrieves.
Retrieval is a data product
For generative AI and agents, the retrieval corpus is often the most important data asset. We work with your stewards to define chunking, metadata, access filters and refresh cadence. A bad corpus produces confident wrong answers. A good one is maintained like any other production dataset.
Lineage and residency
We document data lineage from source to model input to agent action. Residency controls are configured in infrastructure, not in policy documents alone. We support deployment in India, UAE, Australia, EU and US regions.
Evaluation before the first user
An evaluation harness is part of the foundation, not a post-launch afterthought. We build regression suites from your domain: representative queries, edge cases, known failure modes and compliance scenarios. Every model or prompt change runs against that suite before it reaches users.
Working with your data platform team
We are not a replacement for internal data platform ownership. We deliver the AI-specific layer — retrieval corpora, vector indexes, feature pipelines for ML workloads, evaluation datasets — with documentation your team can operate. Handover sessions and pairing during the build are standard.
If your estate lacks a catalogue or lineage tooling, discovery says so and sequences foundation work before agent features. Honest sequencing protects your reputation with the business more than a fast demo.
What we need from you
- A data owner for each source system in scope
- Access to existing catalogues, quality rules and lineage tools — or honesty about gaps
- Agreement on residency and retention requirements before architecture is finalised
- Time from stewards to validate retrieval corpora and evaluation datasets
Questions CDOs ask us
Do you replace our data platform team?
No. We build the AI-specific layer — vector stores, feature pipelines, evaluation datasets and the integrations models need. Your platform team keeps ownership of the core estate. We document everything for handover.
We have a data mesh / fabric initiative. Does that conflict?
It should not. We integrate with domain-owned datasets and your existing access patterns. Discovery maps which domains own which sources for the use case in scope.
How do you handle PII in retrieval and logs?
During discovery we classify data flows and agree masking, redaction and retention rules. Agent logs are configurable — we default to logging actions and outcomes, not full prompt content, unless audit requires otherwise.
How often should retrieval corpora refresh?
Depends on source change rate and risk. Discovery proposes SLAs per corpus — policy daily, product catalogue on change event, static reference monthly — with monitoring when freshness slips.
Increment sequencing for data-heavy estates
When readiness gaps are large, increment one is foundations: lakehouse paths, quality rules, vector index, harness. Increment two is the agent or copilot. That sequencing is explicit in the build proposal so the CDO office is not blamed for a rushed launch.
Related services and reading
AI Discovery and Strategy starts every engagement. Data and AI Foundations, Generative AI and Agent Engineering, ML Engineering and LLMOps, and Application Modernisation follow as scoped increments. How we work describes Perceive, Engineer, Deliver and Run. Insights publishes fortnightly on evaluation, cost, governance and operations.
Named case studies publish when clients allow naming. Until then, role pages, FAQ and Engagements describe delivery honestly — fixed discovery fee credited within sixty days, fixed-scope build increments, managed run optional, handover any time.
We do not invent client logos, outcome statistics or certifications on these pages. Select your region on contact — India, Middle East, ANZ or Other. Glossary defines terms like evaluation harness, agent, residency and increment in plain language.
Getting started
Write to hello@incubics.com with your role, region and use case in two sentences. We respond within one business day with a scoping call invite. Incubics formed in 2026; discovery is two weeks fixed fee; production by week twelve of the build. Contact form regions: India, Middle East, ANZ, Other.
Bring your sponsor, a sketch of systems in scope and honesty about active pilots. We will tell you if discovery is the right next step or if prerequisite work should come first.
Offices in Bengaluru, Pune and the USA. Legal entity: IQLEXA Technologies Private Limited, registered in Pune. Data residency by region: India, Middle East, ANZ, EU, US and other deployments as scoped.
Next step
Start with two weeks.
A fixed-fee discovery gives you a ranked use-case portfolio, a target architecture, a cost model and a build proposal you can take to your board. If we don't find a case worth building, we tell you.