LLM integration
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.
Common focus: Risk and policy assessment before build · Reference architecture shaped before release · Guardrails and telemetry defined with the first scope
What changes
We assess use cases, data exposure, and policy requirements so the LLM roadmap can reflect the organisation's risk appetite.
Blueprints, rollout patterns, evals, logging, and rollback paths are defined before the first release is put in front of users.
Guardrails, observability, and cost controls can be included from the start and reviewed with engineering and compliance owners.
Playbooks, training, and review rhythms keep product, security, and legal aligned after the initial release.
What the work can include
The right scope brings the interface, data, integrations, review points, and handoff into one accountable release.
Lead scope
Codify acceptable use, data handling, and model evaluation policies tailored to your operating environment.
Supporting scope
Design and implement the platform for prompt management, evaluation, monitoring, and controlled release.
Supporting scope
Build high-impact LLM features with engineering discipline and human-in-the-loop controls.
Supporting scope
Keep released experiences measurable with telemetry, evaluations, and product feedback loops.
Illustrative workflow pattern
Embed LLM-powered assistance inside your SaaS product with evaluation harnesses and guardrails.
What this pattern is meant to support: A clearer path to introducing LLM features without losing visibility across product, security, and customer-facing teams.
Assess intents and data exposure with product, legal, and security
Design a retrieval architecture with content filters
Implement prompt versioning and an evaluation harness
Prepare pilot features with staged rollout controls
Monitor quality and iterate with product analytics
Plan an employee assistant around access controls, audit records, and oversight requirements.
Intended support: A way to test whether employees receive useful, grounded answers while agreed access and review controls remain visible.
Use LLMs to synthesise policies, risk reports, and regulatory updates with human sign-off.
Intended support: Compliance teams get clearer audit trails and accountable approval paths while evaluating where LLM support is useful.
These are illustrative delivery patterns. Scope, controls, integrations, and results depend on the operating environment and agreed responsibilities.
A practical starting point
The first conversation should clarify the problem, the operating owner, and the smallest useful next step before a delivery model is chosen.
Deploying LLMs with Confidence
See the reference architecture, eval loops, and telemetry patterns teams should consider before putting LLMs in front of users.
View guideA focused engagement to evaluate use cases, risk posture, and architecture requirements.
Use-case and risk assessment · Reference architecture outline · Controls framework and roadmap
A cross-functional team that designs and builds priority LLM initiatives with guardrails and review points.
Build and staged release planning for priority features · Observability and evaluation setup · Enablement and change management
Continued support to monitor models, manage changes, and review quality, cost, and incidents where longer-term ownership is shared.
Monitoring design and incident review rhythms · Policy and model refresh cycles · Reporting and steering reviews
Questions before a working session
The design can include policy controls, monitoring, approval paths, human review checkpoints, and supporting documentation. The required controls depend on the use case and the organisation's own approval process.
Next step
We’ll review the use case, data exposure, provider options, eval needs, logging, cost limits, and first workflow worth piloting.