LLM integration

LLM features with evals, logging, and cost controls from the start.

Optillium helps product and platform teams connect LLMs to real workflows with retrieval, access rules, evals, logging, fallback paths, and human review where it matters.

Risk and policy assessment in week oneReference architecture delivered by week fourGuardrails and telemetry launched with the MVP
Start a Project BriefingRead the deployment guide

What this solves

Useful for teams caught between product urgency and risk concerns.

Product teams get a delivery path for copilots, assistants, and internal tools that engineering can actually support.

Security and legal teams get clear policy controls, evaluation logic, and change-management visibility instead of promises after the fact.

Leadership gets a roadmap that ties provider choice, cost, risk, and rollout sequencing together.

This service is a fit when teams know they want LLM capability but need a safer path to shipping and owning it long term.

Assessment in the first week

We audit use cases, data exposure, and policy requirements so the LLM roadmap aligns with risk appetite.

Reference architecture in four weeks

Blueprints, deployment patterns, evals, logging, and rollback paths are defined before the first release ships.

Controls and telemetry embedded

Guardrails, observability, and cost controls are implemented from the start to satisfy engineering and compliance teams.

Enablement for long-term ownership

Playbooks, training, and review rhythms keep product, security, and legal aligned after the initial launch.

Capabilities

What gets designed, built, and handed over.

Each engagement ties the workflow, software surface, integration points, review steps, and team handoff together so the first release is usable.

Policy and risk framework

Codify acceptable use, data handling, and model evaluation policies tailored to your operating environment.

  • Use-case triage with risk categorisation
  • Data residency, retention, and redaction rules
  • Third-party provider due diligence templates
  • Review cadence and decision rights

Reference architecture and pipelines

Design and implement the platform for prompt management, evaluation, monitoring, and deployment.

  • Retrieval-augmented generation patterns
  • Prompt versioning and testing harnesses
  • Online and offline evaluation dashboards
  • Multi-region deployment and rollback plans

Application delivery

Ship high-impact LLM features with engineering discipline and human-in-the-loop controls.

  • API, UI, and workflow integration
  • Content moderation and escalation paths
  • Cost optimisation and usage limits
  • Performance tuning with domain data

Observability and continuous improvement

Keep shipped experiences healthy with telemetry, evaluations, and product feedback loops.

  • Safety, quality, and bias monitoring dashboards
  • Experimentation playbooks and KPIs
  • Incident response runbooks
  • Quarterly roadmap and compliance reviews
Delivery scenarios

A few of the ways this service shows up in real operating environments.

Delivery packet

Product copilot launch

1

Assess intents and data exposure with product, legal, and security

2

Design a retrieval architecture with content filters

3

Implement prompt versioning and an evaluation harness

Example delivery pattern

Product copilot launch

Embed LLM-powered assistance inside your SaaS product with evaluation harnesses and guardrails.

  1. 1Assess intents and data exposure with product, legal, and security
  2. 2Design a retrieval architecture with content filters
  3. 3Implement prompt versioning and an evaluation harness
  4. 4Ship pilot features with staged rollout controls
  5. 5Monitor quality and iterate with product analytics

Outcome

Faster roadmap delivery without losing visibility across product, security, and customer-facing teams.

Delivery packet

Internal knowledge assistant

1

Index and classify knowledge sources with access tiers

2

Define redaction, retention, and privacy controls

3

Implement usage monitoring and feedback mechanisms

Example delivery pattern

Internal knowledge assistant

Stand up a secure assistant for employees with access controls, audit logs, and oversight.

  1. 1Index and classify knowledge sources with access tiers
  2. 2Define redaction, retention, and privacy controls
  3. 3Implement usage monitoring and feedback mechanisms
  4. 4Pilot with a targeted group and gather metrics
  5. 5Expand coverage with enablement playbooks

Outcome

Employees get useful answers while sensitive information remains protected and access controlled.

Delivery packet

Risk and compliance automation

1

Aggregate regulations, policies, and control libraries

2

Design prompt templates with compliance review

3

Implement review queues and evidence capture

Example delivery pattern

Risk and compliance automation

Use LLMs to synthesise policies, risk reports, and regulatory updates with human sign-off.

  1. 1Aggregate regulations, policies, and control libraries
  2. 2Design prompt templates with compliance review
  3. 3Implement review queues and evidence capture
  4. 4Deliver dashboards with issue tracking
  5. 5Run quarterly model and policy refresh cycles

Outcome

Compliance teams move faster with clearer audit trails and accountable approval paths.

Engagement models

Start with the level of support the team actually needs.

Assessment

Model

Four-week engagement to evaluate use cases, risk posture, and architecture requirements.

  • Use-case and risk assessment
  • Reference architecture outline
  • Controls framework and roadmap

Build and launch

Model

Cross-functional team that designs, builds, and launches priority LLM initiatives with guardrails.

  • Build and rollout of priority features
  • Observability and evaluation setup
  • Enablement and change management

Managed controls

Model

Ongoing support to monitor models, manage changes, and report quality, cost, and incidents.

  • Continuous monitoring and incident response
  • Policy and model refresh cycles
  • Executive-ready reporting and steering

Related resource

Deploying LLMs with Confidence

See the reference architecture, eval loops, and telemetry patterns teams need before putting LLMs in front of users.

View guideStart a Project Briefing
FAQ

Questions that usually come up before the first working session.

We embed policy controls, monitoring, approval paths, and human review checkpoints into the design. Documentation and evidence are produced alongside delivery work.
We work across OpenAI, Anthropic, Google, AWS, Azure, open-source, and custom fine-tuned models. The architecture depends on data exposure, latency, cost, and ownership constraints.
Yes. We often embed with product and platform teams, handling the specialist evaluation, guardrail, and rollout work while transferring ownership over time.
We set up automated and human evaluations for relevance, safety, cost, and business KPIs, then expose those trends through dashboards and operational review cadences.
Next step

Ready to move one LLM use case from idea to accountable rollout?

We’ll review the use case, data exposure, provider options, eval needs, logging, cost limits, and first workflow worth piloting.

Start a Project BriefingReview resources