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Engineer-led AI strategy: readiness assessment, use-case prioritisation, build vs buy, and a roadmap with real numbers attached. Our cost estimates come from having paid those costs on our own product.
No maturity model. No heat map. A costed, sequenced plan you can fund on Monday.
Can you actually execute? Data availability and quality, engineering maturity to operate what gets delivered, integration surface, compliance constraints, and whether anyone owns the outcome. Readiness predicts success better than model choice does.
Every organisation has twenty AI ideas. We score them on value, feasibility against your actual data, and time to production, then tell you which three to fund and which seventeen to drop.
Per use case, not as a blanket policy. Buy the commodity layer, build only where the differentiation lives. Getting this wrong in either direction is expensive, and vendors on both sides are conflicted.
A number next to each initiative, including the parts everyone forgets: data engineering at 30 to 50 percent of budget, MLOps at 15 to 30 percent annually, and adoption. A roadmap without costs is a wish list.
ISO 42001, EU AI Act risk classification, NIST AI RMF, GDPR, and India DPDP Act mapped to each initiative up front. Governance designed after deployment is remediation.
What to do first, what it unlocks, and what has to be true before the next phase starts. Most programs fail because they attempted phase three before phase one produced anything usable.
Consulting firms rarely say this, so here it is plainly.
Our breakdown of why most AI accelerator and transformation programs fail is in the AI accelerators guide.
For context, consulting-led AI transformation programs from large firms typically run $2M to $30M. Ours are scoped to produce a plan, not to bill a program.
$15k to $50k
3 to 6 weeks
Readiness assessment, use-case prioritisation, build vs buy per case, and a costed roadmap for one business area.
$50k to $150k
6 to 12 weeks
The same across multiple business domains, with sequencing, governance mapping, and an operating model for how AI gets funded and owned.
If the strategy concludes you should build, we can execute it. If it concludes you should buy, we will say so and you owe us nothing further.
Book a free AI audit. We will look at your data, your problem, and your team, and give you a straight answer on what is feasible, what it costs, and what to do first.
A defensible answer to four questions: which problems in your business are genuinely worth solving with AI, whether your data can support them, what each one would cost to take to production, and in what order to sequence them. The deliverable should be a costed roadmap you can fund, not a maturity model. If a strategy engagement ends without a number next to each initiative, it has not done its job.
We are engineers who ship a product, so our strategy work is constrained by what actually gets built. Visylix, our enterprise AI video platform, runs 22 AI analytics and a fully on-premise copilot on customer infrastructure. That means our cost estimates come from having paid those costs, and our feasibility calls come from having hit those walls. Big Four firms bring brand, scale, and change management we do not. If you need a board-facing transformation program across 20 business units, they are a better fit. If you need a roadmap that survives contact with engineering, we are.
A focused readiness assessment and roadmap typically costs $15,000 to $50,000 over 3 to 6 weeks. A broader portfolio strategy across multiple business domains typically costs $50,000 to $150,000. For comparison, consulting-led AI transformation programs from large firms typically run $2,000,000 to $30,000,000. Indian partners price 50 to 70 percent below US and Western European equivalents.
Yes, and we do it regularly. Rules beat machine learning for many problems. Off-the-shelf products beat custom builds for commodity capabilities. Some problems are not learnable with the data available. We would rather tell you that in week two of a strategy engagement than take a build contract we do not believe in and have it fail in month eight.
A structured audit of whether your organisation can actually execute AI: data availability and quality, engineering maturity to operate the delivered systems, integration surface, compliance constraints, and whether there is an accountable owner for outcomes. Readiness is the strongest predictor of whether an AI program succeeds, and it is almost entirely independent of the model or vendor you choose.
Yes. We work to ISO 42001 for AI management systems, the EU AI Act risk classification, the NIST AI Risk Management Framework, GDPR, and India DPDP Act. For regulated buyers we map the applicable sector framework, including HIPAA and CJIS. Governance designed after deployment is remediation, so we scope it into the roadmap rather than bolting it on.