01
Teams stuck between pilot and production
The agent works on clean examples and fails on the real tenant, and nobody can say precisely why.
AI Enablement / Agent-Ready Data
Analysts spent twenty years quietly resolving ambiguity in enterprise data. Agents cannot do that. Where a person would ask which definition was meant, a model picks one and answers confidently. We remove the ambiguity.
Agent-Ready Data
Process and field inventory
Canonical definitions and owners
Maker-checker validation
Handover into your agent stack
It is a different job from the data quality work most enterprises have already done. Cleaning values makes a number correct. Making data agent-ready makes its meaning explicit: what a field holds, which system owns it, how it is calculated, what the exceptions are, and which of three similarly named fields the business actually uses. That gap is why industry surveys put the share of agent pilots reaching production so low, and why fewer than one enterprise in five reports being genuinely ready. The work is unglamorous and it is the difference between a demo and a system.
Who it is for
Teams whose agent work has stalled somewhere between a convincing demo and something they would let touch a customer.
The agent works on clean examples and fails on the real tenant, and nobody can say precisely why.
Accountable for making the estate usable by agents, and holding a catalogue that lists fields without explaining them.
Being asked to sign off an agent that acts on data whose definitions nobody has written down.
How it runs
Six to ten weeks depending on how many applications are in scope. Every phase ends in an artefact your team owns.
We start from the agent you are trying to ship and work backwards to the data it touches, rather than attempting to document the estate.
The standard schema is documented by the vendor. The custom fields, custom workflows and local conventions in your tenant are not, and they are where agents fail.
One agreed meaning per concept, with the source of truth, the calculation, the exceptions and a named owner. Signed off by the function that owns the process.
A maker-checker pass over the labelled set, then handover into your catalogue, graph or agent context layer in a form your systems can read.
Outcomes
Processes and fields as they exist in your instance, including the customisations no vendor documentation covers.
One meaning per concept, agreed by the business, so two agents cannot reach different answers from the same question.
Maker-checker validated and handed into your catalogue, graph or context layer rather than a document nobody opens.
The definitions, owners and validation trail your risk function needs before an agent is allowed to act.
Delivery in India
Indian enterprises rarely fail at AI enablement on capability. They stall on where data goes, which vendors are approved, and who signs off. We settle those questions first so the rollout survives its own audit.
Consent, purpose limitation, and data principal rights are mapped onto model access rules, so approvals hold when your privacy team reviews them.
Open-weight models can run entirely on Indian infrastructure through Gyde Inference when a workload cannot leave the country.
Usage telemetry, model decisions, and policy exceptions are logged in a form your risk and audit functions can present without rework.
We help assemble the security reviews, DPAs, and architecture notes Indian enterprise procurement asks for before a tool reaches production.
Delivered onsite in
Free download
The reference architecture, policy templates, and rollout checklist we use to give an enterprise workforce governed access to AI without opening a data risk.
Questions
If your question is here in a form we have not covered, ask us directly and we will answer it plainly.
Because they fail on meaning rather than on reasoning. Enterprise systems have tolerated ambiguous definitions for decades because a human analyst resolved them. An agent has no such buffer, so where a person would ask which of three revenue fields was meant, the model chooses one and returns a confident wrong answer.
Data quality work makes values correct. A catalogue lists what exists. Neither states what a field means, how it is calculated, which exceptions apply or who owns it. Agents need that semantic layer, and it is usually the piece nobody has written down.
Years of adoption work inside live enterprise tenants. We have mapped more than 250 processes and at least 2,000 fields across SAP SuccessFactors modules including EC-Core, Time Off, LMS, PMGM, Compensation, Payroll Integration and Time Tracking, alongside Salesforce and Oracle work, in real customer environments rather than sandboxes.
SAP SuccessFactors and S/4HANA, Salesforce and Oracle are where our mapping experience is deepest. We scope other applications case by case, and we will say plainly when an application is outside what we have actually worked in.
It solves half of it. SAP and Workday both publish semantic layers over their standard data models, and those are genuinely useful. What no vendor can describe is your tenant: the custom fields, the local conventions and the workflow variations your organisation added. That half is the half agents trip over.
Six to ten weeks for a focused scope covering the workflows behind one or two agents. Documenting an entire estate takes far longer and is rarely worth it, so we deliberately work backwards from the agent you are trying to ship.
You do. The maps, definitions and validated datasets we produce for you are yours, handed over in a form your catalogue, graph or context layer can consume. We keep our own methods and templates, and your data is never reused elsewhere.
Yes, and it is most of what we do. The work runs on IST with teams onsite across Bengaluru, Mumbai, Delhi NCR, Pune, Hyderabad and Chennai. Where the DPDP Act 2023 or an RBI or IRDAI expectation applies, mapping runs against de-identified or representative data unless you specifically agree otherwise.
Keep going
Start with one agent
We will scope the data behind it and tell you what has to be true before it can go to production.