AI Consulting & Strategy
FISTA Solutions provides AI consulting from a delivery practice rather than a slide practice: opportunity assessment grounded in your data and processes, honest feasibility, a sequenced roadmap with cost estimates, governance appropriate to your risk — and the engineering team to build what the roadmap recommends.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does AI consulting and strategy include?
AI consulting work covers current-state and data readiness assessment, opportunity identification with business impact, feasibility and cost assessment per opportunity, a sequenced roadmap, governance and risk approach, and capability planning for the team that must run it.
- 01
Readiness assessment
Data, systems, and process readiness assessed honestly, because most AI failure traces here.
Assessment - 02
Opportunity identification
Candidate use cases with estimated business impact, built from how the work actually happens.
Opportunity - 03
Feasibility and cost
Technical feasibility and cost per opportunity from a team that has built similar systems.
Feasibility - 04
Sequenced roadmap
Order based on value, feasibility, and dependency, with the platform work that later items need.
Roadmap - 05
Governance and capability
Risk-proportionate governance and a plan for the skills your team needs to operate what is built.
Operating model
Requirements
Which requirements shape AI consulting and strategy?
AI strategy is only useful if it survives contact with delivery. Requirements cover honest data readiness, feasibility assessed by people who build, value estimates tied to real process metrics, dependency-aware sequencing, and governance sized to actual risk.
| Requirement | Why it matters | How FISTA builds to it |
|---|---|---|
| Data readiness honesty | Most AI failure is data failure. | Direct assessment of data quality, access, and governance rather than accepting an optimistic self-report. |
| Feasibility from builders | Strategy without delivery grounding overpromises. | Feasibility and cost assessed by engineers who have built comparable systems. |
| Value grounded in process | Benefit estimates drift into fiction. | Impact estimated from measured process metrics — cycle time, volume, error rate — not from category benchmarks. |
| Dependency sequencing | Ambitious first projects fail. | Sequencing that puts platform and data prerequisites before the use cases that depend on them. |
| Proportionate governance | Heavy governance stalls; none creates risk. | Governance sized to the risk of each use case, with lighter paths for low-risk internal tools. |
Where AI fits
How should you sequence AI consulting and strategy?
Useful AI strategy is short and specific: assess readiness honestly, identify opportunities from real process observation, test feasibility with builders, sequence around dependencies, and start delivering the first item while the roadmap is still warm.
- 01
1. Assess readiness honestly
Data, systems, and process reality, because optimistic assumptions here invalidate everything after.
- 02
2. Find opportunities in the work
Observed processes and measured pain, not a workshop list of aspirations.
- 03
3. Test feasibility with builders
Engineers who have built similar systems assessing what is actually achievable and at what cost.
- 04
4. Sequence around dependencies
Platform and data prerequisites before the use cases that require them.
- 05
5. Start building
Deliver the first item immediately, because strategy that sits unimplemented ages quickly.
Cost and timeline
How much does AI consulting and strategy cost, and how long does it take?
Consulting is scoped to the assessment needed rather than sold by the month; timeline is typically weeks. FISTA does not quote blind: the scoping call returns an assessment scope, deliverables, and a fixed estimate.
The engagement is deliberately short. A few weeks of focused assessment produces a roadmap that can be acted on; longer engagements tend to produce documents that age before anything is built.
The most valuable output is often the list of things not to do. Recommending against poorly-suited use cases saves more than any single recommendation to proceed.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI system?
FISTA delivers AI in four phases: a discovery sprint that defines the success metric, data readiness, and specification; a design that fixes the model strategy, retrieval, guardrails, and evaluation plan; iterative builds scored against a golden set; and a production release with tracing, dashboards, cost budgets, and a change process.
- 1
Discover and define
Use-case selection, data audit, success metrics, risk review, and a written specification with an evaluation plan.
OutputSpecification, golden set, estimate
- 2
Design the system
Model strategy, retrieval and data pipelines, guardrails, human review points, and the deployment target.
OutputArchitecture, model decision record
- 3
Build and evaluate
Two-week increments, each scored on the evaluation harness for quality, latency, and cost, demoed on real data.
OutputEval reports, working system
- 4
Release and monitor
Production deployment with tracing, quality and cost dashboards, drift alerts, runbooks, and a change process that re-runs the evals.
OutputProduction AI system with SLOs
Why FISTA
Why choose FISTA Solutions for AI consulting and strategy?
FISTA consults from a delivery practice: feasibility and cost come from engineers who build these systems, and the roadmap can be executed by the same team. Work is contracted through a US entity with full IP assignment.
AI Consulting specifics
- Feasibility and cost estimates come from engineers who have built comparable systems, not from category benchmarks.
- Data readiness is assessed directly, because optimistic assumptions about data are the most common cause of AI failure.
- Sequencing accounts for platform and data dependencies, so the first project is achievable rather than impressive.
- The recommendation explicitly includes the use cases to avoid, which is usually the highest-value part.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What buyers ask before an AI build.
Straightforward guidance for evaluating scope, fit, and the next step.
01How is this different from a strategy firm?
Feasibility and cost estimates come from engineers who build these systems, and the same team can deliver the roadmap. Strategy produced without delivery grounding tends to overpromise on both timeline and outcome.
02How long does an AI assessment take?
Typically a few weeks. Longer engagements usually produce documents rather than progress, and the roadmap ages while it is being written.
03What if our data is not ready?
Then that is the finding, and the roadmap sequences data work first. Recommending AI use cases on top of inadequate data is how programs waste a year.
04Will you recommend against AI?
Frequently, for specific use cases where rules, process change, or an existing product would do better. That list is usually the most valuable part of the assessment.
05Can you build what you recommend?
Yes, and that is the point. The roadmap is written by the team that would deliver it, which keeps feasibility and cost honest.
Scoped in writing before you commit
Get a roadmap the same team can deliver.
Bring your processes, data, and ambitions. The assessment returns opportunities, feasibility verdicts, and a sequenced plan.