Custom AI Development
FISTA Solutions builds custom AI for problems that off-the-shelf tools do not fit: domain-specific pipelines, models adapted to your data, integration with the systems the outcome depends on, and evaluation tied to a business metric rather than a benchmark.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does custom AI development include?
Custom AI work starts with problem framing and data assessment, selects the simplest approach that can work, builds the pipeline or model, evaluates against a business metric, integrates with the systems that carry the outcome, and leaves operations in place.
- 01
Problem framing
The decision or outcome being improved, stated with the metric that will show whether it worked.
Framing - 02
Data assessment
Whether your data can support the approach, honestly, before any build commitment is made.
Data - 03
Approach selection
The simplest method that can work — rules, classical ML, LLM, or a combination — chosen by evidence.
Method - 04
Build and evaluation
Implementation with evaluation against the business metric rather than only a technical score.
Build - 05
Integration and operations
Connection to the systems that act on the output, with monitoring and retraining where relevant.
Production
Requirements
Which requirements shape custom AI development?
Custom AI is worth building only when the problem is genuinely specific and the data supports it. Requirements cover honest framing, data adequacy, the simplest viable approach, business-metric evaluation, and integration so the output actually changes something.
| Requirement | Why it matters | How FISTA builds to it |
|---|---|---|
| Honest framing | Vague problems produce unusable systems. | The decision being improved and the metric that proves it, agreed in writing before the build. |
| Data adequacy | No data, no model. | Data volume, quality, and labeling assessed before commitment, with data work scoped separately if needed. |
| Simplest viable approach | Sophistication is not the goal. | Rules and classical methods considered before LLMs, because simpler approaches are cheaper to run and easier to explain. |
| Business-metric evaluation | Technical scores can improve while outcomes do not. | Evaluation tied to the business metric, with technical metrics as diagnostics rather than the target. |
| Integration | An unintegrated model changes nothing. | Connection to the systems and workflows that act on the output, planned as part of the build. |
Where AI fits
How should you sequence custom AI development?
Before building custom AI, exhaust the alternatives: a product, a simpler method, or better process. What remains after that filter is usually a genuinely specific problem where custom work pays off.
- 01
1. Check for a product
If something off the shelf solves it adequately, that is almost always cheaper and faster.
- 02
2. Frame the decision
What decision improves and by what measure, written down before any approach is chosen.
- 03
3. Assess the data
Whether your data supports the approach, with data work scoped separately if it does not.
- 04
4. Try the simplest method
Rules and classical ML before LLMs, because simpler is cheaper to run and easier to defend.
- 05
5. Integrate and measure
Connection to the workflow that acts on the output, with the business metric tracked.
Cost and timeline
How much does custom AI development cost, and how long does it take?
Cost is driven by data readiness and integration depth more than by modeling; timeline by data assessment and access. FISTA does not quote blind: the scoping call returns a framing, a data assessment, and an honest build recommendation.
Data work usually dominates. Assembling, cleaning, and labeling data is the larger share of most custom AI projects, and it is scoped explicitly rather than absorbed into a modeling estimate.
The honest answer is sometimes not to build. FISTA will say when a product, a process change, or a simpler method addresses the problem better than a custom system would.
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 custom AI development?
FISTA frames the problem before choosing a method, assesses whether your data supports it, and prefers the simplest approach that works. Work is contracted through a US entity with full IP assignment.
Custom AI specifics
- The decision being improved and its metric are agreed in writing before any approach is selected.
- Data adequacy is assessed honestly before commitment, with data work scoped separately when it is needed.
- Simpler methods are tried before complex ones, because they are cheaper to operate and easier to explain.
- Evaluation is tied to the business metric, with technical scores used as diagnostics rather than as the goal.
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.
01When is custom AI worth it?
When the problem is specific to your domain, data, or workflow and no product solves it adequately, and when your data can support the approach. FISTA checks both before recommending a build.
02Do we need machine learning, or will an LLM do?
It depends on the problem. Classical methods are often cheaper, faster, and more explainable for structured prediction; LLMs suit unstructured language and reasoning tasks. FISTA chooses by evidence rather than fashion.
03What if our data is not ready?
Then data work comes first and is scoped separately. Building on inadequate data produces a system that appears to work in development and fails in production.
04How do you know it worked?
By tying evaluation to the business metric agreed during framing, with technical scores as diagnostics. A model whose accuracy improves while the business metric does not has not succeeded.
05How long does custom AI take?
Data assessment takes weeks; build and evaluation depend on approach and integration depth. Discovery produces a phased plan with the data work visible.
Scoped in writing before you commit
Build custom AI only where it genuinely fits.
Bring the problem and your data. The scoping call returns a framing, a data assessment, and an honest build recommendation.