What we do

LLM Deployment & RAG

Private language models and retrieval systems connected to organizational knowledge, with access controls, evaluation, and human review designed into delivery.

Discuss a project

Designed around your operating context

Move beyond a prompt demo to a governed AI capability that can be tested, operated, and improved. We design language-model applications around the work they need to support, the information they may use, and the decisions they must leave to people.

This service suits teams that need answers or drafting grounded in internal material, but cannot treat every source or action as public. Typical starting points include knowledge search, service-desk assistance, document workflows, and domain-specific copilots. We first establish whether retrieval, a model change, conventional software, or a combination is the right tool for the job.

Where it helps

Use cases with a clear owner and outcome

  • Knowledge and policy search

    Index approved handbooks, procedures, product documentation, or engineering references. Return source passages and document links so staff can verify an answer instead of accepting an unsupported response.

  • Support and service copilots

    Retrieve account- or product-specific context, classify a request, and draft a response for an agent. Keep customer-visible replies, credits, and other consequential actions behind the organization’s chosen approval rules.

  • Document intake and extraction

    Extract candidate fields from contracts, forms, or reports, then validate required values and route exceptions to the responsible reviewer. Use deterministic checks for fields and calculations that must be exact.

  • Engineering and operations assistants

    Search runbooks, incident notes, and system documentation to prepare summaries or suggest next steps. Limit access to the relevant knowledge domain and preserve links to the evidence used.

System design

Architecture and decisions that matter

A production RAG system is a chain of data and decision boundaries. The exact components depend on source systems, identity, data sensitivity, latency, and hosting requirements; the model is only one part of the design.

  • Identity-aware source access

    Inventory content owners, permissions, freshness, and sensitivity. Ingest only approved sources, retain document identifiers and metadata, and filter retrieval against the caller’s access before context reaches the model.

  • Ingestion and retrieval

    Normalize files and records, split content using document structure, attach provenance, and select embedding and search strategies against representative queries. Hybrid or metadata-filtered retrieval may be more useful than vector search alone.

  • Model and tool boundary

    Route requests through a controlled application layer that applies prompt versions, output schemas, rate limits, and approved tools. Keep secrets and authorization decisions in server-side services, not in model instructions.

  • Evaluation and feedback

    Build a versioned question set with expected evidence and acceptable outcomes. Measure retrieval relevance, answer grounding, refusal behavior, latency, and cost; capture reviewed feedback to guide changes.

Acceptance and review

Define what good looks like

Success measures are agreed with the people who own the outcome. These are useful signals to consider, not a promise of a particular result.

  • Evidence coverage

    How often useful answers include relevant, authorized source material.

  • Boundary behavior

    Whether the system abstains, refuses, or escalates in defined edge cases.

  • Task quality

    Reviewer-rated correctness on an agreed representative evaluation set.

  • Operational fit

    Latency, usage cost, support load, and observed failure patterns.

What you receive

A delivery package your team can operate

  • Discovery brief covering users, tasks, source systems, data classes, constraints, and measurable acceptance criteria.
  • Documented architecture and data-flow decisions, including identity boundaries, retrieval design, model options, and deployment assumptions.
  • Working application or integration with source references, validated outputs, and a clear path to human review for uncertain or restricted work.
  • Evaluation set and test results for representative questions, edge cases, access boundaries, and prompt-injection attempts relevant to the use case.
  • Operational handover describing ownership, monitoring, content refresh, incident escalation, and how to review or roll back changes.

Enterprise delivery

Built for review, operation, and change

“Enterprise-ready” is not a certification claim or a one-size-fits-all stack. Scope, controls, service levels, and compliance responsibilities are agreed against your environment and policies.

  • Security and data boundaries

    Agree access roles, data classification, approved integrations, retention, and secret handling with the relevant owners. Implement controls in the application and platform where possible; do not rely on policy text alone.

  • Testable acceptance

    Translate requirements into reviewable scenarios, including ordinary use, invalid input, denied access, dependency failure, and recovery. Keep decisions and outstanding assumptions documented.

  • Production operations

    Identify service owners, useful logs and metrics, alert routes, escalation expectations, and runbooks before launch. Monitoring and support scope are agreed for each engagement.

  • Controlled change

    Use separated environments, reviewed changes, deployment checks, and a practical rollback or disable path appropriate to the system. Document who can approve and operate changes.

How we work

A phased path from discovery to handover

  1. 01

    Frame the task

    Map users, current workflow, failure cost, source-of-truth systems, and what a correct answer or safe abstention means. Agree on a baseline and acceptance criteria before selecting a model.

  2. 02

    Prove retrieval quality

    Use a representative, permissioned sample to compare chunking, metadata, filters, and retrieval approaches. Review misses with domain owners and fix source quality or access design before adding more autonomy.

  3. 03

    Build the controlled path

    Implement the user flow, model gateway, structured outputs, validation, citations, and escalation. Integrate only the tools needed for the task and keep consequential writes outside the free-form reasoning loop.

  4. 04

    Evaluate and operate

    Test quality, safety, access, load, and cost against agreed scenarios. Release in a limited mode, review real outcomes, and hand over dashboards, runbooks, ownership, and change procedures.

Good to know

Questions to consider

Do we need to fine-tune a model?

Usually not as the first step. Retrieval is often the better fit when answers need current organizational information and traceable sources. Fine-tuning is considered when behavior or format remains inadequate after retrieval and prompting have been evaluated against a suitable dataset.

How do you reduce unsupported answers?

No design can promise that a language model will never be wrong. We test retrieval and grounding, expose sources, validate structured output, provide abstention and escalation paths, and keep higher-impact actions under review.

Can the system respect existing permissions?

It can be designed to use identity and access metadata from the relevant source systems. The exact enforcement point depends on those systems; permission behavior must be tested with representative roles before sensitive sources are enabled.

Can it run in our cloud or private environment?

Potentially. Hosting options depend on model availability, data requirements, identity, network boundaries, and operational ownership. We document trade-offs before recommending a deployment path.