Choose whether to share optional analytics.

FISTA Solutions does not load Google Analytics until you accept. Rejecting keeps optional analytics off. Read the Cookie Policy.

Generative AI

Generative AI Development

FISTA Solutions builds generative AI systems for production rather than demonstration: outputs grounded in your data, brand and factual constraints enforced in the pipeline, human review where stakes require it, evaluation that catches regressions, and cost modeled per unit of output.

Start an AI project Get a scoped quote
150+
projects delivered
50+
companies served
99.9%
verified uptime
47%
efficiency gains
12+
countries reached

What we build

What does generative AI development include?

Generative AI work covers use-case selection with a measurable outcome, grounding and prompt architecture, constraint and safety layers, human review workflow, evaluation harnesses with golden sets, and deployment with tracing and cost controls.

  1. 01

    Grounded generation

    Outputs grounded in your approved data and content, so generation is constrained rather than free-form.

    Grounding
  2. 02

    Constraint layer

    Brand voice, prohibited claims, and format rules enforced in the pipeline rather than requested in a prompt.

    Control
  3. 03

    Review workflow

    Approval interfaces designed for speed, because review throughput decides whether generation volume helps.

    Review
  4. 04

    Evaluation harness

    Golden sets and scoring run in CI, so prompt and model changes are measured before release.

    Quality
  5. 05

    Provenance and tracing

    Model, prompt version, and reviewer recorded per generated artifact, with full traces retained.

    Accountability

Requirements

Which requirements shape generative AI development?

Generative systems fail on accuracy, consistency, and unreviewed volume. Requirements cover grounding in real data, enforced constraints, review capacity that matches output volume, measurable quality, and provenance for anything published.

Generative AI: requirements and how FISTA Solutions builds to them
RequirementWhy it mattersHow FISTA builds to it
GroundingUngrounded generation invents facts.Retrieval or structured data grounding with citations, and abstention where evidence is missing.
Enforced constraintsPrompt instructions are not controls.Claim lists, format validation, and brand rules enforced in the pipeline with rejection on violation.
Review capacityVolume without review is risk.Review interfaces designed for batch approval with exception flagging, sized against generation volume.
Measurable qualityQuality drifts silently.Golden sets with scoring in CI, plus production sampling to catch what offline evaluation misses.
ProvenancePublished content must be attributable.Model, prompt version, inputs, and approver recorded per artifact and retained.

Where AI fits

How should you sequence generative AI development?

Start where output is high volume, reviewable, and currently expensive in human time. Generation earns its place when it removes drafting effort from people who then review rather than write, and where a wrong output is caught before it matters.

  1. 01

    1. Pick reviewable, high-volume work

    Drafting tasks where a human already reviews, so the agent changes effort rather than accountability.

  2. 02

    2. Ground it

    Real data behind every output, with citations where claims are made.

  3. 03

    3. Encode the constraints

    Brand, claim, and format rules written down and enforced, not left to prompt phrasing.

  4. 04

    4. Design the review

    Approval flow built with the generation, because review throughput caps the value.

  5. 05

    5. Measure and monitor

    Golden-set evaluation in CI plus production sampling to catch drift.

Cost and timeline

How much does generative AI development cost, and how long does it take?

Cost is driven by output volume, grounding complexity, and review workflow; timeline by content rules capture and evaluation build. FISTA does not quote blind: the scoping call returns a design, an evaluation plan, and a cost model.

Generation cost scales with usage rather than revenue, so cost per generated unit is modeled during design and controlled through caching, routing, and output discipline.

Review is the real throughput limit. Generating ten thousand drafts is cheap; approving them is not, which is why the review interface is designed alongside the generation pipeline.

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 quote

Delivery

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. 1

    Discover and define

    Use-case selection, data audit, success metrics, risk review, and a written specification with an evaluation plan.

    Output

    Specification, golden set, estimate

  2. 2

    Design the system

    Model strategy, retrieval and data pipelines, guardrails, human review points, and the deployment target.

    Output

    Architecture, model decision record

  3. 3

    Build and evaluate

    Two-week increments, each scored on the evaluation harness for quality, latency, and cost, demoed on real data.

    Output

    Eval reports, working system

  4. 4

    Release and monitor

    Production deployment with tracing, quality and cost dashboards, drift alerts, runbooks, and a change process that re-runs the evals.

    Output

    Production AI system with SLOs

Why FISTA

Why choose FISTA Solutions for generative AI development?

FISTA builds generative systems grounded in your data, constrained by enforced rules, and measured by evaluation rather than impression. FISTA is an official Anthropic partner with production experience across model providers.

Generative AI specifics

  • Outputs are grounded in approved data with citations, and the system abstains rather than inventing when evidence is missing.
  • Brand and claim rules are enforced in the pipeline with rejection on violation, not requested politely in a prompt.
  • Golden-set evaluation runs in CI, so prompt or model changes cannot silently degrade output quality.
  • Provenance — model, prompt version, inputs, approver — is recorded per artifact and retained.

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 do you stop generative AI inventing facts?

By grounding generation in retrieved or structured data, requiring citations for factual claims, validating output against rules, and designing abstention so the system says it does not know rather than producing something plausible.

02Can generated content be published without review?

For low-risk internal content sometimes; for anything public or customer-facing, FISTA builds approval into the pipeline. Provenance is recorded either way so published artifacts are attributable.

03How do you measure generation quality?

With golden sets scored in CI on criteria that matter for the task, plus sampling of production output. Without measurement, quality claims are impressions and drift goes unnoticed.

04What does it cost to run?

It scales with output volume, which is why cost per generated unit is modeled during design and managed with caching, routing, and output length discipline.

05How long until a generative system is in production?

A focused use case typically takes weeks to a quarter, with content rule capture and evaluation set assembly as the usual critical path.

Continue exploring

Related capabilities

LLM application developmentThe application engineering behind generative features.RAG development servicesThe retrieval layer that grounds generation.Marketing AI agentsContent production agents with brand governance.

Scoped in writing before you commit

Generate at volume without publishing a problem.

Bring the content workflow and your rules. The scoping call returns a design, an evaluation plan, and a cost model.

Start an AI project Browse all ai development