Approved tool catalogue
Evaluate and select the AI tools your organisation will sanction, with a clear path for requesting anything outside the list.
AI Enablement
Training tells people what to do. Enablement decides what the systems will let them do. We build the approved tool catalogue, the access model, the guardrails, and the telemetry that turn scattered AI usage into something governed and measurable.
What we put in place
Approved tool catalogue
Identity and access
Model routing and cost
Guardrails and data boundaries
Usage telemetry
Policy and governance
It answers the questions training cannot: which tools are approved, who can reach which models, what data may cross which boundary, what happens when someone tries something the policy forbids, and how usage is evidenced for audit. Adoption changes what people do. Enablement changes what the systems allow. An organisation that runs training without enablement produces a trained workforce with no sanctioned way to apply the training, which is the most common reason adoption programmes stall in their second quarter.
Capabilities
Each one can be delivered on its own. Together they form the layer between your workforce and the models they use.
Evaluate and select the AI tools your organisation will sanction, with a clear path for requesting anything outside the list.
Single sign-on, group-based entitlements, and domain restriction, so access follows the same rules as every other enterprise system.
Route each request to the right model on quality, latency, cost, and policy, with budgets and alerts per team.
Enforce what may be sent to which model, with redaction, blocked categories, and a defined path for regulated data.
Who used what, for which task, with what outcome. The record your risk and audit functions will ask for.
The written AI usage policy, exception process, and review cadence, aligned to your existing governance rather than bolted beside it.
In production
Engineering was the first function most enterprises enabled properly, so it is where our worked examples are deepest.
The problem
These are the four conditions we most often find when an organisation has trained its people and adoption has still gone sideways.
With no sanctioned tool, people use their own. Company data leaves through a channel nobody can see, log, or stop.
Uncoordinated tool and model selection across business units produces duplicated spend and a security review for each one.
Without enforced rules, the burden of judgement falls on individual employees, which is neither fair to them nor defensible in an audit.
Usage exists but is unlogged, so the organisation cannot show what was used, by whom, on which data, or under which approval.
The engagement
Eight to sixteen weeks depending on scope. Every phase ends with a decision your governance function can record.
Weeks 1 to 3
Weeks 4 to 7
Weeks 8 to 12
Weeks 13 to 16
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.
AI enablement is the systems layer that gives an organisation safe, governed, and measurable access to AI at work. It covers the approved tool catalogue, identity and access, model routing, guardrails and data boundaries, usage telemetry, and the governing policy. It answers what the systems will allow, which is a separate question from what people have been trained to do.
Adoption is the people layer and enablement is the systems layer. Adoption changes what people do through training, workshops, and leadership alignment. Enablement changes what the systems allow. Running one without the other is the usual failure mode: training with no governed access leaves people unable to apply what they learned, and access with no training leaves the tools unused.
No. Most organisations end up with a small approved catalogue rather than one tool, because engineering, customer support, and finance have genuinely different needs. What matters is that the catalogue is deliberate, that access follows one identity model, and that every tool in it reports usage the same way.
Eight to sixteen weeks depending on scope. Three weeks to assess current and shadow usage, four to design the catalogue and controls with security and privacy sign-off, five for a pilot with two or three teams on real work, and four to scale on the pilot evidence.
Data classification and residency are settled during the assessment phase, before any tool is approved. Where a workload cannot leave India, open-weight models can run entirely on Indian infrastructure through Gyde Inference. Consent, purpose limitation, and data principal rights are mapped onto the access rules so approvals survive a privacy review.
Usage telemetry showing who used what and for which task, the model decisions and routing applied, guardrail events including blocked attempts, and a record of policy exceptions and their approvals. It is assembled in a form your risk and audit functions can present without reworking it first.
Yes. Most engagements start with an inventory that includes tools already in use, whether formally procured or not. Anything that meets the security and telemetry bar stays in the catalogue, since replacing a working tool for consistency alone is rarely worth the disruption.
Enablement puts the access, control, and evidence layer in place. Building the applications, workflows, and agents that run on top is our full-stack AI consulting practice, covering apps and workflows, routing, inference, fine-tuning, and evals. The two are usually sequenced together.
Keep going
Design the layer
Bring us your current tool sprawl and your regulatory obligations, and we will scope an enablement pilot around both.