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libraTechnologies

Services

Enterprise AI engineering, from foundations to production.

Libra works with enterprise teams to establish AI foundations, productionise prototypes and build reusable platforms, evaluations and integrated workflows.

Discuss an AI priority

Six service areas

Six ways we help enterprise AI move forward.

Each service is scoped against the organisation's systems, controls, operating context and evidence. These are capabilities, not claims of prior delivery.

AI Enablement

Establish the architecture, tooling, operating model and team capability needed to adopt AI effectively.

Typical output: Foundation map, target architecture and adoption roadmap.

AI Prototype to Production

Turn an existing PoC, demo or prototype into a secure, scalable and monitored production system.

Typical output: Production-readiness plan, deployment path and operating controls.

AI Platform Engineering

Build the cloud infrastructure, model access, RAG patterns, identity, observability and deployment paths your teams can reuse.

Typical output: Reference platform, reusable patterns and engineering guardrails.

Agentic Workflow Development

Build multi-step AI workflows integrated with enterprise systems, approvals and exception paths.

Typical output: Workflow design, system integrations and human-control points.

LLM Evaluation Frameworks

Measure quality and reliability with evals, benchmarks, regression tests, guardrails and failure analysis.

Typical output: Evaluation suite, quality baselines and release gates.

AWS AI Consulting

Design and implement AWS-centric AI systems using services such as Bedrock, SageMaker and cloud-native infrastructure.

Typical output: AWS architecture, implementation plan and production foundation.

Production claims follow evidence.

Architecture, integration, evaluation, security and operating controls are scoped for each environment. Representative examples are not client work, completed outcomes or guarantees.

Human owners retain approval for consequential business, engineering, operational and safety decisions.

Technology context.

Enterprise AI work may touch cloud, model, data, security and business systems. These names indicate technical context only, not partnership, certification, a prebuilt integration or prior delivery.

Cloud and platform

  • AWS
    Cloud platform a workflow's systems may run on.
  • Microsoft Azure
    Cloud platform a workflow's systems may run on.
  • Google Cloud
    Cloud platform a workflow's systems may run on.
  • OpenAI
    Model provider that may appear as an option in a decision record.
  • Anthropic
    Model provider that may appear as an option in a decision record.

Security and data

  • Cloudflare
    Edge and access controls that may constrain a workflow.
  • CrowdStrike
    Endpoint security that may constrain a workflow.
  • Zscaler
    Access controls that may constrain a workflow.
  • Snowflake
    Data platform a workflow's records may sit in.
  • Databricks
    Data platform a workflow's records may sit in.

Operational and business systems

  • GitHub
    Where supporting code or automation may already live.
  • ServiceNow
    Service and workflow system that may be a touchpoint.
  • SAP
    ERP that may be a touchpoint in the workflow.
  • Atlassian
    Work-tracking system that may be a touchpoint.
  • Supabase
    Application backend that may be a touchpoint.
  • Vercel
    Hosting a supporting application may use.

Technology names and marks belong to their respective owners. Their inclusion indicates only that a system may be relevant to scoped engineering work. It does not imply partnership, certification, endorsement, a prebuilt integration or delivery experience.

Bring the prototype, platform or workflow that needs to become dependable.

The first conversation is a direct review of the problem, production constraints and sensible next step.

Discuss an AI priority