CyberFreezeDev
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A short, ranked AI plan based on how work actually flows through your business.
Disappointing AI projects usually aim at the wrong problem. We find where AI saves real hours, then rank by value, effort and risk.
Most AI projects that disappoint were aimed at the wrong problem. A chatbot gets built when the real cost was invoices typed in by hand. A team buys five AI subscriptions and nobody changes how they work. A pilot impresses in a demo and then stalls because the data it needs sits in someone's email.
AI strategy consulting is the step that prevents that. We spend time with the people who do the work, look at the documents and systems they touch, and find the places where AI would save real hours or reduce real errors. Then we rank those places by value, effort and risk, and tell you plainly which ones to build, which ones an existing product already does, and which ones are not worth doing yet.
The work is led by Sai Dharshan S, our founder and AI consultant, whose background includes Deloitte USI, Botminds AI and Integrate.com, together with the engineers who would build what we recommend. That matters: the plan is written by people who know what each item costs to deliver, because they deliver it.
What the business wants to change this year sets what counts as value.
What the business is trying to change this year: growth, margin, response time, compliance load, hiring pressure. This sets what counts as value.
We watch the real work and note every re-keyed number and every wait.
We sit with the people who handle orders, invoices, enquiries, reports and approvals, and watch the work happen. We note every copy and paste, every re-keyed number and every wait for someone's reply.
Where your data lives, how clean it is, who owns it and what connects.
Where the data lives (Tally, spreadsheets, a CRM, WhatsApp, email, paper), how clean it is, who owns it, and what can be connected through an API.
Every candidate listed with current effort, AI approach, systems involved and risks.
Every candidate written down with the current effort, the AI approach, the systems involved and the risks.
Use cases scored and sequenced: quick wins, then data foundations, then larger builds.
Each use case scored on the criteria below and placed in a sequence: quick wins, foundation work such as cleaning up data, then larger builds.
The top item written as a buildable spec you can hand to anyone.
The top item written up as a buildable spec: scope, success measure, data needed, who reviews the output, and what it should cost to run. You can hand it to us or to anyone else.
| Criterion | The question we ask | Why it matters |
|---|---|---|
| Hours or errors removed | How much time does this take now, and what does a mistake cost? | Sets the ceiling on value |
| Data readiness | Is the input already digital and reachable, or on paper and in inboxes? | Most delays in AI projects are data work |
| Tolerance for mistakes | What happens if the AI is wrong one time in twenty? | Decides whether a person must review every output |
| Build or buy | Does a product already do this well, such as an accounting or CRM tool with AI built in? | Buying is often faster and cheaper than building |
| Data protection | Does it involve personal data under the DPDP Act 2023, UK GDPR or GDPR? | Decides where the model may run and what must be recorded |
| Ownership | Who on your team will own it after launch? | A system with no owner decays within months |
Whether data can go to a hosted model or must stay on your hardware.
Whether your data can go to a hosted model under a processing agreement, or whether it should stay on your own hardware. If private, we size it with our private AI deployment team.
Which AI tools you already pay for are used, and which overlap.
An honest look at the AI tools already being paid for across the company, which ones are used, and which ones overlap.
When n8n is enough and when a workflow needs a custom build.
When a workflow tool such as n8n is enough, and when a custom build is needed. See AI workflow automation.
How roles change, what never goes into a public chatbot and who needs training.
How roles will change, what data must never be pasted into a public chatbot, and what training each team needs. Our AI training workshops follow on from this.
Use cases the EU AI Act may regulate, flagged early for your legal adviser.
If you sell AI features to customers in the EU, the EU AI Act sets duties by risk level. We flag where a use case may fall into a regulated category so your legal adviser can review it early.
No. The roadmap and pilot spec are yours. Many items will be products you can buy, and the spec is written so any competent team can build from it.
The engagement scales to the company. For a firm of 10 people it can be a few conversations and a short written plan. The value is in avoiding the subscription or the build that would not have paid off.
Where they fit, we say so and say that they are ours: Fint for invoicing and GST, Desk for CRM and projects, Cybe Social for scheduling. Where another product fits better, we recommend that instead.
Time with the people who do the work, sample documents with sensitive fields masked if you prefer, read access or screenshots of the main systems, and one person on your side who owns the outcome.
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