Stage 11 to 2 weeks
AI Opportunity Sprint
Teams who know AI matters but not where to start.
A short, structured discovery that finds the AI opportunities worth pursuing in your business, and the ones that aren't.
Find your AI opportunityAI evaluation & LLMOps
AI evaluation and managed operations for production systems
Launching an AI system is the start, not the finish. We evaluate, monitor, secure and improve AI systems in production so quality stays measurable, costs stay predictable and model changes do not surprise you.
Works for systems we build and for AI features your team or another vendor already shipped.
What usually goes wrong
AI systems do not stay the same after launch. Models are updated or retired, your data changes, users find new edge cases and costs creep up. Without evaluation and monitoring, you find out when a customer complains.
Quality that drifts silently
A prompt tweak or provider-side model update changes behaviour, and nobody notices until answers are wrong in production.
Model upgrades that feel risky
New models could be cheaper or better, but without a test suite nobody can prove it, so teams stay on ageing versions.
Costs no one owns
Token spend grows with usage and longer prompts. Without per-feature visibility, bills arrive before explanations.
Governance questions without answers
Leadership, customers or auditors ask who can access the system, what it logged and how it is controlled, and the evidence is scattered.
Where Managed AI Operations earns its keep
Evaluation suite for an existing AI feature
Build a representative test set and automated scoring so every change is measured before release.
Production quality monitoring
Sample and score live outputs, track user feedback and alert on drops in quality or spikes in failures.
Guardrails and safety layer
Add input and output checks, topic limits, personal-data redaction and prompt-injection defences around an existing system.
Cost and latency optimisation
Profile usage, introduce caching and model routing, trim context and set budgets without sacrificing measured quality.
Model migration
Move to a newer or different model with side-by-side evaluation, staged rollout and a rollback plan.
AI governance evidence
Access reviews, audit trails, data-flow documentation and incident procedures ready for internal or customer review.
What we actually build
Evaluation datasets and scorers
Curated test cases from real usage, with rubric-based, reference-based and model-graded scoring where each fits.
CI checks for AI changes
Evaluations run automatically in your delivery pipeline, blocking releases that regress beyond agreed limits.
Observability and tracing
Request traces, token usage, latency, error rates and feedback in dashboards your team can read.
Guardrail services
Reusable pre- and post-processing checks for safety, format, personal data and policy compliance.
Access and audit controls
Role-based access to AI tools and admin functions, with tamper-evident logs of prompts, outputs and actions.
Runbooks and reviews
Incident response steps, monthly quality and cost reviews, and a roadmap of improvements.
Systems we connect to
The usual suspects. If yours has an API or a database, we can almost certainly work with it.
- Model providers
- Anthropic, OpenAI, Azure OpenAI, AWS Bedrock, Google Vertex AI
- Observability
- OpenTelemetry, LLM tracing tools, Grafana, Cloud monitoring
- Delivery
- GitHub Actions, GitLab CI, Docker, Feature flags
- Security
- SSO / identity providers, Secrets managers, Cloud IAM, Log archives
How it works, step by step
- 01
Baseline
We review the system, its data flows and risks, then build an evaluation set that reflects how it is really used.
- 02
Instrument
Tracing, cost tracking and feedback capture are added so every request can be understood later.
- 03
Guard
Guardrails and access controls are added where the risk review shows they are needed.
- 04
Monitor
Live outputs are sampled and scored, with alerts for quality, cost or latency moving outside agreed ranges.
- 05
Improve
Monthly reviews turn findings into changes to prompts, retrieval, models or UX, each proven on the evaluation set before release.
Blueprint: safe model upgrade
Reference design, not a client projectThe scenario: A support assistant runs on an older model. A newer model promises lower cost, but the team cannot risk worse answers.
- 1. TriggerCandidate modelNew model or prompt version proposed
- 2. AI stepOffline evaluationBoth versions scored on the same test set
- 3. Rules & checksRelease gateQuality, cost and latency limits checked
- 4. Human controlOwner sign-offResults reviewed with the product owner
- 5. System updateStaged rolloutTraffic shifted gradually, rollback ready
- Trigger
- AI step
- Rules & checks
- Human control
- System update
How we deliver it
Timings are typical for a first release. They depend on scope, how ready your data is and the integrations involved, so we confirm them after discovery.
- 1Weeks 1 to 2
AI system review
We assess architecture, prompts, data flows, access, costs and failure modes, and deliver a prioritised risk and improvement list.
- 2Weeks 2 to 4
Evaluation & observability setup
We build the test set, scorers, dashboards and alerts, and wire evaluations into your release process.
- 3Weeks 3 to 6
Guardrails & controls
We implement the highest-priority controls from the review and document them.
- 4Monthly
Managed operations
Ongoing monitoring, monthly quality and cost reviews, model and dependency updates, and support when something goes wrong.
Safeguards, built in from day one
Security, privacy, testing and human control are designed in from the start, not bolted on after a pilot. We adapt them to your policies and your risk.
Evaluation before every change
Prompts, retrieval settings and models are versioned. Nothing ships without an evaluation run against the agreed thresholds.
Human-in-the-loop by design
Review queues and approval steps are monitored too. We track override rates to spot where the AI needs help or where humans are rubber-stamping.
Access controls and audit trails
Who can use, configure and view the logs of each AI system is explicit, reviewed and recorded.
Incident readiness
Kill switches, fallbacks and a documented incident process let you turn off or degrade an AI feature quickly and safely.
What you can expect
The goal is AI you can reason about. We report against baselines agreed in the review, typically covering:
- Quality measured on every release instead of assumed
- Model upgrades decided on evidence
- Predictable, attributable AI spend
- Faster detection and diagnosis of problems
- Clear governance evidence for leadership and customers
What we measure
Evaluation scores per release, Live quality samples and feedback, Cost per request and per feature, Incidents and time to resolve.
We don't promise savings or accuracy figures up front. We measure them on your data during the pilot.
Where it fits best
- SaaS & digital products. Customer-facing AI features that must not regress.
- Education. Student-facing assistants that need safe, appropriate answers.
- ERP & operations. Automations that touch financial records.
- Logistics. Agents that communicate with customers on your behalf.
Ways to begin
Stage 23 to 5 weeks
Workflow Automation Pilot
One valuable workflow you want to prove before scaling.
A focused pilot that automates a single workflow end to end, running against a measured baseline so the result is a decision, not a demo.
Start an AI pilotStage 36 to 12 weeks
Production AI Build
An AI product or feature you are ready to ship to real users.
A full build from product design to deployment: the AI, the application around it, the integrations and the tooling to run it.
Plan a production buildStage 4Ongoing, monthly
Managed AI Operations
AI systems already in production that need an owner.
Ongoing care for production AI: we watch quality, control costs, handle model changes and keep security reviews current.
Talk about managed AI
AI Evaluation and Managed Operations: questions we get asked
What is an AI evaluation suite?
An AI evaluation suite is a set of representative test cases plus automated scoring that measures how well an AI system performs. Running it before every change shows whether quality improved, stayed the same or regressed.
Can you take over an AI system another team built?
Yes. We start with a system review, then add evaluation, monitoring and controls around the existing system before making larger changes.
What are AI guardrails?
Guardrails are checks around an AI model, on its inputs, outputs and actions, that enforce format, safety, privacy and business policy, for example blocking personal data leaks or out-of-scope requests.
How do you reduce AI costs without hurting quality?
We measure first, then apply caching, smaller models for simpler steps, shorter context, batching and budgets, and check each change on the evaluation suite so savings do not cost accuracy.
Do you offer ongoing support after launch?
Yes. Managed AI Operations is a monthly engagement covering monitoring, reviews, model updates, cost controls, security reviews and support.
Already running AI in production?
Tell us what it does and what worries you about it. We'll suggest where evaluation, monitoring or guardrails would help most. We reply within one business day.
We reply within one business day, and we're happy to sign an NDA first. Prefer email? Write to info@originsphere.in.