LLMOps Platform Development
FISTA Solutions builds the operational practice around AI systems: versioned prompts, models, and tools, golden-set evaluation running in CI, canary and shadow releases, end-to-end tracing, drift detection, and a rollback path that has been tested rather than assumed.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does LLMOps platform development include?
LLMOps work covers versioning for prompts, models, and tool definitions, golden-set evaluation in CI with scoring, canary and shadow release mechanics, tracing across the full request path, drift and quality monitoring, and documented rollback procedures.
- 01
Versioning
Prompts, models, tool definitions, and configuration versioned together, so a release is one identifiable thing.
Control - 02
Evaluation in CI
Golden sets scored on every change, with thresholds that block a release when quality regresses.
Quality - 03
Canary and shadow releases
New versions exposed to a slice of traffic or run alongside production before full rollout.
Release - 04
Tracing
End-to-end traces covering prompts, retrievals, tool calls, and outputs with sensitive data redacted.
Observability - 05
Drift and quality monitoring
Production quality signals and drift alerts, so degradation surfaces before users report it.
Monitoring - 06
Rollback
A tested rollback path, because the ability to revert quickly is what makes shipping safely possible.
Safety
Requirements
Which requirements shape LLMOps platform development?
AI systems are non-deterministic and their dependencies change underneath you. Requirements centre on making behavior measurable, changes versioned and reversible, production quality observable, and provider-side changes detectable before users find them.
| Requirement | Why it matters | How FISTA implements it |
|---|---|---|
| Measurable behavior | You cannot manage what you do not measure. | Golden sets from real cases with scoring appropriate to the task, run on every change in CI. |
| Version everything | Prompt edits are code changes. | Prompts, models, tools, and configuration versioned together and deployed as a single identifiable release. |
| Provider-side drift | Models change without your release. | Scheduled evaluation runs against production configuration, alerting when scores move without a deploy. |
| Production signals | Offline evaluation is not enough. | Quality proxies, escalation rates, and user signals monitored alongside offline scores. |
| Tested rollback | Untested rollback is a hope. | Rollback exercised regularly so reverting a bad release takes minutes rather than an incident call. |
Where AI fits
How should you sequence LLMOps platform development?
Introduce LLMOps in the order that makes each next step possible: build the evaluation set, version what is deployed, add tracing, then release gradually and monitor production quality once you can see it.
- 01
1. Build the golden set
Real cases with expected behavior, because nothing downstream works without a quality measure.
- 02
2. Version the release unit
Prompts, models, and tools versioned together so a change is identifiable and revertible.
- 03
3. Add tracing
Full request traces with redaction, so failures can be diagnosed rather than guessed at.
- 04
4. Release gradually
Canary or shadow exposure with automatic rollback triggers on quality or error signals.
- 05
5. Monitor and re-evaluate
Production quality signals plus scheduled re-evaluation to catch provider-side drift.
Cost and timeline
What does LLMOps platform development cost, and how long does it take?
Cost is driven by the number of AI systems covered and evaluation depth; timeline by golden-set assembly. FISTA does not quote blind: the scoping call returns an LLMOps design and a phased estimate.
Golden-set assembly is the main effort and the main value. Collecting real cases with agreed expected behavior takes time from people who know the domain, and nothing else in LLMOps works without it.
The practice pays back across systems. The second and third AI workload inherit the evaluation, release, and observability machinery, which is why LLMOps is worth building as a platform rather than per project.
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 deployment?
FISTA deploys AI in four phases: an assessment that inventories workloads, data boundaries, and constraints and produces a target architecture; a platform build with networking, identity, secrets, and observability as code; a migration with evaluation gates and shadow traffic; and a production cutover with dashboards, budgets, runbooks, and rollback.
- 1
Assess and target
Workload inventory, data classification, latency and volume profile, compliance constraints, and a target architecture with cost model.
OutputTarget architecture, cost model
- 2
Build the platform
Networking, identity, key management, model endpoints, gateway, tracing, and evaluation pipeline delivered as infrastructure-as-code.
OutputPlatform as code, control matrix
- 3
Migrate with gates
Move applications behind the gateway, run evaluation and shadow traffic, and tune routing, caching, and capacity.
OutputEval reports, shadow results
- 4
Cut over and operate
Graduated production rollout, dashboards for quality, latency, and cost, runbooks, on-call, and a change process with rollback.
OutputProduction platform with SLOs
Why FISTA
Why choose FISTA Solutions for LLMOps platform development?
FISTA builds LLMOps as a working practice rather than a dashboard: evaluation that blocks releases, versioning that makes changes identifiable, and rollback that has actually been tested. Work is contracted through a US entity with full IP assignment.
LLMOps specifics
- Golden-set evaluation runs in CI with thresholds that block releases, not as an optional report nobody reads.
- Prompts, models, tools, and configuration are versioned as a single release unit, so any change is identifiable.
- Scheduled evaluation against production configuration catches provider-side drift between your own deploys.
- Rollback is exercised regularly, so reverting is a routine action rather than an incident improvisation.
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 platform teams ask before deploying AI.
Straightforward guidance for evaluating scope, fit, and the next step.
01What is LLMOps actually?
The operational practice that makes AI behavior measurable, changes versioned and reversible, and production quality observable. Concretely: golden-set evaluation in CI, versioned releases, tracing, drift detection, and tested rollback.
02Why do we need evaluation in CI?
Because prompt, model, and tool changes alter behavior in ways code review cannot catch. Without evaluation gating releases, quality drifts silently until a user complains.
03How do we detect when a provider changes a model?
Scheduled evaluation runs against your production configuration, alerting when scores move without a deploy on your side. That is the only reliable signal for provider-side change.
04How large should a golden set be?
Large enough to cover your real case distribution including edge cases, which is usually dozens to low hundreds of cases per task rather than thousands. Coverage matters more than volume.
05How long does it take to establish?
A working practice for one system typically takes weeks, with golden-set assembly as the main effort. Subsequent systems are much faster because the machinery is reused.
Scoped in writing before you commit
Make AI changes deliberate, measured, and reversible.
Bring the systems already in production. The scoping call returns an LLMOps design, an evaluation plan, and a phased estimate.