AI Strategy & Transformation
Most AI projects fail because they start with a tool instead of a business problem. We help leadership teams pick the few use cases that will move revenue, cost or risk, prove them on real data and scale them safely.
What this covers
Typical timeline
Assessment and roadmap in 2–4 weeks; first pilot results within 6–8 weeks.
Who it is for
Our consultants combine hands-on engineering with process know-how, so every recommendation is buildable. You get an honest view of what AI can and cannot do for your business, what it will cost to run, and the data, security and people changes needed to make it stick.
Discuss your requirementCEOs and boards asked "what is our AI strategy?"
Operations heads drowning in manual, repetitive work
CTOs choosing between OpenAI, Gemini, Claude and open-source models
Regulated businesses that need AI with DPDP-compliant data handling
Capabilities
A structured review of processes, data quality, systems and skills that scores where AI can help and what must be fixed first.
Workshops with each department to list opportunities, then a value-versus-effort matrix with realistic savings and payback periods.
Side-by-side evaluation of GPT, Claude, Gemini and open-source models on your own sample tasks, including cost per request and data residency.
Guidelines for data access, retention, consent and human review aligned with the DPDP Act, plus an acceptable-use policy for staff.
A time-boxed proof of concept with success metrics agreed upfront, so the decision to scale is based on evidence.
Role-wise enablement, AI champions and adoption tracking so new tools are actually used after launch.
How we work
You always know what happens next, who is responsible and what you will receive at each stage.
Typical timeline
Assessment and roadmap in 2–4 weeks; first pilot results within 6–8 weeks.
Goals, constraints, risk appetite and budget are clarified with decision makers.
We shadow key workflows and sample the data they depend on.
Opportunities are scored on value, feasibility and risk.
A 6–12 month plan with costs, owners and KPIs is presented.
The top use case is built and measured on live work.
Deliverables
Technology & standards
We recommend tools based on your scale, budget and existing systems, not on what is fashionable. Every choice is explained in the proposal.
Engagement models
A 3–4 week pilot on your own data with agreed accuracy targets, so you see real results before scaling.
Hardened integration, guardrails, monitoring and admin controls, delivered in milestones with a fixed quote.
Ongoing prompt and model tuning, evaluation runs, cost monitoring and feature additions on a monthly plan.
How pricing works: AI projects are priced in two stages: a fixed-price proof of concept, then a production quote based on what the pilot proves. Model and API usage costs are estimated upfront and billed at actuals.
Get a quoteFAQs
A prioritised list of AI use cases with estimated savings, the architecture and vendors to use, a governance policy and a phased roadmap with costs. Where useful we also build a pilot so you see measured results before committing a larger budget.
Usually yes, because mid-sized firms have repetitive work in sales, support, finance and operations that AI can shorten quickly. We keep the engagement small, typically a few weeks, and focus only on use cases with a clear payback.
It depends on the task, data sensitivity and budget. We test shortlisted models on your own sample work and compare accuracy, speed and cost per request. Sensitive data can stay on open-source models hosted in your own cloud account.
We classify the data each use case needs, minimise personal data, set retention rules and make sure vendors do not train on your data. The governance plan documents consent, access and human review so you can demonstrate compliance.
Both. The same team that designs the roadmap builds chatbots, agents, RAG systems and automations, which keeps recommendations practical. You are also free to execute the roadmap with your own team.
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