AI Development
LLMOps & Observability Services
We implement LLMOps so AI features are measurable and maintainable: tracing, evals, prompt/versioning, feedback loops, and guardrails that prevent silent regressions.
Overview
What this service is
We instrument your AI workflows end-to-end: prompts, retrieval, tool calls, model routing, and user outcomes—so you can see exactly what happened.
We build evaluation harnesses and regression gates so changes to prompts, data, or models are tested before they impact users.
You get dashboards for latency, cost, and quality, plus operational playbooks that make incident response and iteration far more predictable.
Benefits
What you get
Fewer production regressions
Evals and release gates catch quality drops before they reach customers.
Actionable visibility
Tracing shows which step failed—retrieval, tool call, or model response—so fixes are targeted.
Cost and latency control
Monitor usage and optimize routing, caching, and prompt size to keep budgets stable.
Faster iteration cycles
Versioning + test data lets teams improve safely without fear of breaking workflows.
Operational readiness
Alerts, dashboards, and playbooks turn AI into an owned, maintainable system.
Features
What we deliver
Tracing and analytics
Capture prompt inputs, retrieval results, tool calls, outputs, and user outcomes with correlation IDs.
Prompt + configuration versioning
Manage prompt changes like code: versions, rollbacks, and staged rollout controls.
Eval suites + regression gates
Golden datasets, automated scoring, and CI checks to prevent quality drift.
Feedback loops
Thumbs up/down, reasons, and sampling to build a roadmap for continuous improvements.
Cost/latency optimisation
Caching, streaming, and model routing strategies to hit performance and budget targets.
Safety monitoring
Guardrails, policy checks, and anomaly detection for risky outputs and tool misuse.
Process
How we work
Instrumentation plan
We define events, metrics, and trace points aligned to your workflows and KPIs.
Tracing + dashboards
We implement logging, tracing, and dashboards for latency, cost, and quality.
Eval harness
We build eval datasets and automated scoring integrated into CI/release gates.
Iteration loop
We add feedback capture, sampling, and playbooks for continuous improvements.
Tech Stack
Technologies we use
Core
Tools
Use Cases
Who this is for
Teams shipping RAG assistants
Measure retrieval quality, citation coverage, and answer helpfulness across real queries.
Tool-calling agents
Track tool-call correctness, failure rates, and approval outcomes for reliable automation.
Multi-model routing
Route by cost/latency needs with dashboards that show real spend and performance.
High-risk domains
Add stricter quality gates, safety checks, and audits for regulated or sensitive workflows.
Scaling usage post-launch
Add operational guardrails so traffic growth doesn’t create surprise costs or instability.
FAQ
Frequently asked questions
Not always. For features that impact customers or costs, basic tracing and a small eval set usually pay off quickly.
Yes. We can instrument existing workflows and progressively introduce evals and release gates without a rewrite.
Quality (helpfulness/accuracy), latency, cost, failure modes, tool-call correctness, and safety events—tailored to your use case.
Yes. Caching, prompt tightening, and model routing typically reduce spend while improving speed.
Yes. We deliver docs, dashboards, and playbooks so observability becomes part of normal engineering operations.
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Need production visibility for your AI features?
Share your current stack and user journeys—we’ll propose an LLMOps plan to reduce regressions and improve quality safely.
Quality gates + dashboards included.