Opportunity mapping
We review your product, workflows, and data to identify where AI creates measurable value, then rank each candidate by impact, feasibility, and risk.
001AI Strategy Consultation
AI strategy consultation that ends in a build plan, not a slide deck
We help CTOs, founders, and product leaders decide where AI actually belongs in their product and operations, and where it does not. The engagement ends with a prioritized roadmap, a candidate architecture, and cost estimates your engineering team can act on the following week.
AI005
What you get
What the consultation covers
Every engagement is run by engineers who ship AI systems to production, so the advice stays grounded in what current models can reliably do.
Data readiness audit
We assess whether the data you have can support the use cases you want: volume, quality, labeling, access, and the gaps you would need to close first.
Build, buy, or API decision
For each use case we compare fine-tuned models, off-the-shelf APIs, and vendor products on cost, accuracy, latency, and lock-in, and recommend one path.
Architecture and model selection
You get a candidate architecture: which models, how retrieval and orchestration fit together, and how the system connects to your existing stack.
Cost and risk modeling
We estimate inference and infrastructure costs at your projected volumes and flag the failure modes, hallucination risks, and compliance questions that matter for your case.
Prioritized roadmap
The final deliverable is a sequenced plan: what to pilot first, what to defer, and what to skip, with effort estimates your team can budget against.
How it works
How we run the engagement
(4)- 1
Discovery
We interview your technical and business stakeholders, review your systems and data, and agree on the outcomes the strategy has to serve.
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Use case evaluation
We test the highest-value candidates against your real data where possible, so recommendations rest on evidence rather than vendor claims.
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Strategy and roadmap
We write up the architecture, the build-versus-buy calls, the cost model, and a sequenced roadmap, then walk your team through every decision.
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Handoff or build
Your team can execute the plan on their own, or we move straight into a pilot with the same engineers who wrote the strategy.
Why Webisoft
Why teams bring us in for AI strategy
We are a software studio first, so our advice is shaped by what survives contact with production, not by what demos well.
Engineers, not analysts
The people advising you build and ship AI systems. Every recommendation is one we would be willing to implement ourselves.
Honest about limits
If a use case is not ready for current models, or a plain rules engine would do the job cheaper, we say so and put it in writing.
Vendor neutral
We have no reseller agreements, so model and platform recommendations are driven by your requirements and your budget, nothing else.
Strategy that connects to delivery
Because we build what we recommend, the roadmap comes with realistic effort estimates instead of optimistic placeholders.
FAQ
Common questions
(4)- Most AI strategy engagements run two to six weeks. The timeline depends on how many use cases are being evaluated, how accessible the organization's data is, and how quickly stakeholders can be interviewed. Smaller assessments focused on a single department finish faster, while enterprise-wide roadmaps that touch many systems sit at the longer end. Scoping the timeline up front, before work begins, is standard practice.
- No. Assessing the current state of the data is normally part of the consultation itself. If gaps exist, a good strategy documents what needs to be fixed, in what order, and at roughly what cost, so data readiness becomes a planned step on the roadmap rather than a blocker. Many organizations discover during this phase that some use cases are viable immediately while others require months of data work first.
- A typical engagement ends with a written strategy document covering prioritized use cases, a candidate technical architecture, build-versus-buy recommendations, a cost model, and a sequenced roadmap with effort estimates. The document should be concrete enough to hand directly to an engineering team, whether internal or external. Executive-level summaries and ROI projections are often included so leadership can approve funding for the first phase.
- Generally no. A sound strategy evaluates commercial APIs and open-weight models against the organization's accuracy, latency, privacy, and cost requirements rather than defaulting to one vendor. When two options are close, the trade-offs should be documented so the business can decide. Designing for portability from the start also keeps switching costs low as the model landscape changes.
Where we add value
What an AI strategy engagement with Webisoft covers
Most AI initiatives fail at selection, not implementation: the wrong use case, the wrong build versus buy call, or no path from demo to production. Our consultation work exists to get those decisions right before serious money is spent.
Use case triage
We score your candidate AI ideas against three filters: business value if it works, technical feasibility with current models, and the cost of being wrong. The output is a ranked shortlist with the reasoning written down, which usually kills a few popular ideas and surfaces one or two unglamorous winners.Build, buy, or API decisions
We compare calling hosted models like Claude or GPT against open weight models you host, and against off the shelf products, with real numbers: per request cost at your volume, latency, data exposure, and lock-in. Most companies need less custom model work than they think, and we say so when that is the case.Data readiness assessment
AI quality is capped by the data behind it. We audit what you actually have: where it lives, how clean it is, what consent and licensing terms attach to it, and what a retrieval or fine-tuning pipeline would need. You get a gap list with remediation effort attached, before those gaps become production incidents.Proof of concept with exit criteria
We build narrow working prototypes on your data, defined by pass or fail criteria agreed in advance: accuracy thresholds, latency budgets, cost per transaction. A POC that cannot fail is theater. Ours are designed so a negative result is cheap and a positive one carries directly into a production plan.Governance and risk controls
We define where human review is mandatory, how model outputs are logged and evaluated, what data can and cannot reach third party APIs, and how you comply with privacy law such as Law 25 and GDPR. This is what lets legal and security sign off instead of blocking the project at the finish line.Roadmap and internal capability
The engagement ends with a sequenced roadmap: what to ship in the next quarter, what to defer, what to staff for, with budget ranges per phase. We also train your engineers on evaluation harnesses and prompt and pipeline design, so the second project needs less of us than the first.
How it runs
How an AI strategy consultation unfolds
(4)- 1
Discovery and opportunity mapping
One to two weeks of interviews with business owners and a review of your systems and data. We collect every AI idea in the building, add candidates your team has not considered, and document current pain in measurable terms so later results can be compared against a baseline. - 2
Feasibility and cost modeling
For the top candidates we test feasibility hands-on: sample your data, run it through candidate models, and measure quality on a small labeled set. In parallel we model running costs at your real volumes, because a use case that works at demo scale can be uneconomic at ten thousand requests a day. - 3
Pilot build
We build the strongest candidate as a working pilot integrated with real data, evaluated against the pass or fail criteria set earlier. Deliverables include the pilot itself, an evaluation report, and an honest recommendation: proceed, adjust scope, or stop. Stopping here costs a fraction of stopping in production. - 4
Roadmap and handover
We close with a written strategy: prioritized initiatives, architecture recommendations, governance policy, team and budget implications, sequenced over quarters. You can execute it internally, with us, or with another vendor. The document is written to survive that choice, with reasoning included rather than just conclusions.
FAQ
Questions buyers ask about AI strategy work
(6)- A focused engagement typically runs four to eight weeks. The main cost drivers are how many use cases we evaluate in depth, whether a working pilot is included, and how much data preparation your environment needs before anything can be tested. We scope it in phases, so discovery alone is a small commitment and you decide about the pilot with the feasibility evidence in hand rather than up front.
- For most companies, hosted models with good retrieval and prompt design cover the first several use cases at far lower cost and risk than training anything. Custom fine-tuning or self-hosted open weight models earn their complexity when you have strict data residency requirements, very high volumes where per token pricing dominates, or a narrow task where a small tuned model beats a general one. We run that comparison with your numbers, not industry folklore.
- First we classify what data the use case actually needs, and strip or mask what it does not. Then we match the remainder to provider terms: enterprise API tiers that exclude training on your data, region pinning where residency matters, or self-hosted models when nothing may leave your infrastructure. Everything is written into a data flow document your security team reviews before any real data touches an external endpoint.
- Every use case gets an evaluation harness before it gets a deployment: a labeled test set drawn from your real data, quality metrics agreed with the business owner, and cost and latency budgets. Post launch, outputs are sampled and scored on a schedule, because model behavior drifts as providers update models and as your inputs change. If a system cannot be measured, we advise against shipping it, however impressive the demo.
- The failures we see most: picking a use case for its optics rather than its economics, underestimating data cleanup, skipping evaluation so nobody can tell if the system degrades, and ignoring the workflow change needed for staff to trust the output. Our process attacks each directly: value scoring in triage, a data audit before commitment, mandatory evaluation harnesses, and involving the end users during the pilot instead of at launch.
- Start with a scoping call, after which we send a short intake: your candidate ideas, the systems and data sources involved, and any hard constraints such as compliance regimes or vendor policies. Useful preparation is naming one accountable sponsor and gathering examples of the documents, tickets, or records the AI would work with. Real samples in the first workshop save weeks, because feasibility arguments end quickly when the actual data is on the table.
