Claude Development
FISTA Solutions is an official Anthropic partner building production systems on Claude: applications and agents, tool and MCP integrations, context and caching architecture designed for cost and quality, evaluation harnesses, guardrails, and observability — including in FISTA's own engineering practice.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does Claude development include?
Claude development covers use-case design, context and caching architecture, tool and MCP integration with permission scoping, agent design with approval gates where needed, evaluation harnesses, guardrails and failure handling, and production observability with cost attribution.
- 01
Context and caching architecture
What Claude sees and in what order, with caching designed so cost and quality improve together.
Design - 02
Tool and MCP integration
Tools exposed with typed schemas and least-privilege scoping, often through MCP servers.
Tools - 03
Agent design
Multi-step workflows with approval gates, traces, and evaluation where the system must act rather than answer.
Agents - 04
Evaluation harness
Golden sets scored in CI, so prompt, tool, and model changes are measured before release.
Quality - 05
Guardrails and observability
Failure and refusal handling, plus traces, quality signals, and cost attribution per workload.
Operations
Requirements
Which requirements shape Claude development?
Building on any frontier model well means engineering around it: measured quality, disciplined context, permission-correct tools, graceful failure handling, and cost that scales with value rather than with enthusiasm.
| Requirement | Why it matters | How FISTA builds to it |
|---|---|---|
| Measured quality | Prompt and model changes shift behavior. | Golden-set evaluation in CI covering normal, edge, and adversarial cases, with release thresholds. |
| Context discipline | Context is the main cost and quality lever. | Deliberate context architecture with caching for stable content and volatile content placed last. |
| Permission-correct tools | Tools can bypass application security. | Tool access scoped to the caller and enforced where tools execute rather than requested in a prompt. |
| Failure handling | Timeouts and refusals reach users. | Timeouts, retries, fallbacks, and refusal handling designed into the application rather than discovered. |
| Cost attribution | Spend must map to value. | Cost per request and per completed task tracked per workload, with budgets and alerts. |
Where AI fits
How should you sequence Claude development?
Start with one workload where the outcome is measurable, build the evaluation set before tuning prompts, ship behind guardrails, and widen once the platform pieces — evaluation, tracing, cost attribution — are reusable.
- 01
1. Choose a measurable workload
One workflow with a baseline, so the first deployment proves something specific.
- 02
2. Build the evaluation set
Golden cases from your own data before prompt tuning, or improvement cannot be verified.
- 03
3. Design context and tools
Context architecture and tool scoping settled before feature work accumulates around them.
- 04
4. Ship behind guardrails
Approval gates, fallbacks, and refusal handling in place before real users arrive.
- 05
5. Reuse the platform
Evaluation, tracing, and cost attribution reused for each subsequent workload.
Cost and timeline
How much does Claude development cost, and how long does it take?
Cost is driven by integration depth, evaluation rigor, and inference volume; timeline by data access and evaluation assembly. FISTA does not quote blind: the scoping call returns an architecture, an evaluation plan, and a cost model.
Context architecture is the main engineering lever on cost. Caching stable content and keeping volatile content last changes workload economics substantially, and it is designed rather than discovered.
The second workload is much cheaper than the first. Evaluation harnesses, tracing, guardrail patterns, and cost attribution are reused, which is why the initial investment should be made properly.
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 Claude development?
FISTA is an official Anthropic partner that builds on Claude with evaluation, guardrails, and cost attribution as standard — and runs the same practices internally rather than only recommending them to clients.
Claude Development specifics
- Official Anthropic partner, with Claude in client production systems and in FISTA's own AI-native engineering process.
- Every workload ships with golden-set evaluation in CI, so behavior changes are measured rather than assumed.
- Context and caching architecture is designed for cost and quality together rather than assembled feature by feature.
- Tool access is scoped and enforced where tools execute, with approval gates on anything consequential.
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.
01What does official Anthropic partner mean for our project?
In practice it means Claude is a first-class part of FISTA's practice rather than an occasional integration, including inside FISTA's own engineering process, so the patterns come from daily use rather than documentation.
02Should we use the API directly or through a cloud platform?
Direct access is simplest with the broadest feature availability; a cloud platform can suit procurement, data governance, or existing commitments better. FISTA recommends against your constraints and records the trade-offs.
03How do you keep quality stable over time?
Golden-set evaluation in CI plus scheduled runs against production configuration, so both your changes and provider-side changes are caught before users notice them.
04Can you work with our existing Claude implementation?
Yes, starting with an assessment of context architecture, evaluation coverage, guardrails, and cost, with a written statement of what should be addressed before new features land.
05How long until something is in production?
A focused workload typically reaches supervised production within weeks to a quarter, depending on integration depth and how much evaluation data must be assembled.
Scoped in writing before you commit
Build on Claude with the engineering around it.
Bring the workload and the outcome. The scoping call returns an architecture, an evaluation plan, and a cost model.