LLM & generative AI for business

Make your knowledge useful in the moment.

Turn company documents and approved business data into practical AI assistants. We design retrieval, model integration and review workflows around the questions your team actually needs to answer.

  • Your knowledgeConnected to approved sources
  • Controlled accessPermission-aware retrieval
  • Tested responsesEvaluation before rollout
See how it connects
RAGanswers grounded in sources
Private / clouddeployment options
Human reviewfor important decisions
Measuredquality, latency and cost
LLM & Generative AI Solutions

Useful AI starts with a specific job

Search a policy, draft a response or find a record. Start with one clear use case and an agreed way to measure whether it helps.

Knowledge-base assistants

Search approved policies, manuals and documents with retrieval-augmented generation and source references.

Private & local LLM setup

Evaluate local or hosted models against data requirements, available infrastructure and operating budget.

Customer & team assistants

Build conversational interfaces with clear limits, escalation paths and human handover.

Business tool integrations

Connect to supported CRM, helpdesk and internal APIs with scoped access and approval rules.

Evaluation & fine-tuning

Test representative questions first. Consider fine-tuning only when the task and available data justify it.

Team training & handover

Document how to use, review and maintain the assistant—including when not to rely on its output.

Designed to work together

Connect the model to context—not guesswork.

A model is one part of the system. Source quality, permissions, retrieval and response checks determine whether the experience is useful for your team.

  • Approved sources with clear ownership and update rules
  • Retrieval filtered to each user’s access
  • Source references and explicit uncertainty
  • Human approval before consequential actions
KNOWLEDGE → CONTEXT → RESPONSEIllustrative architecture

An assistant with a source of truth.

A representative RAG workflow for internal business knowledge.

01 INPUTS & CONNECTIONS
DocumentsPolicies & manuals
Business dataApproved records
Team questionUser & access scope
Connect & validate
02 / RETRIEVEPermission-aware retrieval

Find relevant passages from your knowledge index

Controlled processing
03 / GENERATELLM + response checks

Use retrieved context, cite sources, flag uncertainty

Approval required before business actions
Approved output
Cited answersLinks to evidence
Draft contentReady for review
Tool actionsScoped & approved
Trace sources, evaluate answers and log approved tool activity.Example architecture · Final design depends on requirements and system access.
01 / Knowledge search & RAG

Find answers in the information you already own.

For teams searching policies, manuals or internal documentation. Retrieval-augmented generation supplies relevant source material to the model so answers can point back to evidence.

What we can build for you

  • Approved source ingestion and update ownership
  • Access-filtered retrieval and source references
  • Evaluation using representative business questions
CONNECTED TO APPROVED KNOWLEDGE

Company knowledge assistant

TEAM MEMBER

How do I submit a purchase request?

ASSISTANT / EXAMPLE RESPONSE

The purchasing guide lists the request and approval steps.

Source: Purchasing guide · Review before acting
Ask a follow-up question ↑

AI output needs review. Sources and permissions matter.

Designed for the in-between, too.Loading-state concept
Illustrative UI prototype · Sample content, not a client project
02 / Private & local LLMs

Choose where your model and data are handled.

For organisations evaluating on-premise, private-cloud or hosted models. We compare capability, operating cost and infrastructure needs against your data-handling requirements.

What we can build for you

  • Model and deployment-option assessment
  • Infrastructure, access and configuration planning
  • Testing for latency, quality and operating constraints
CONNECTED WORKFLOW

A private model environment

Follow the information from input to outcome.

01 / INPUTAuthorised team & approved data
02 / PROCESSPrivate model endpoint & access controls
03 / OUTPUTReviewed responses & operational logs
Access controls · Review steps · Activity logs
Designed for the in-between, too.Loading-state concept
Illustrative workflow · Sample content, not a client project
03 / Customer & employee chatbots

Make routine questions easier to handle.

For service and support teams answering repeated questions. A chatbot needs a defined scope, a maintained knowledge source and a clear handover when it cannot help.

What we can build for you

  • Conversation flows and approved knowledge sources
  • Website or internal-tool integration
  • Escalation, human handover and conversation review
CONNECTED TO APPROVED KNOWLEDGE

Support assistant

TEAM MEMBER

Can I update a submitted request?

ASSISTANT / EXAMPLE RESPONSE

I can help locate the request policy or hand this to your team.

Handover available · Example conversation
Ask a follow-up question ↑

AI output needs review. Sources and permissions matter.

Designed for the in-between, too.Loading-state concept
Illustrative UI prototype · Sample content, not a client project
04 / Document drafting & summarisation

Give people a first draft—not a final decision.

For teams preparing summaries, responses or internal documents. We build workflows that use the right source material and put review before publication or delivery.

What we can build for you

  • Defined templates and approved source inputs
  • Draft generation with source context where available
  • Human review, editing and version history
YOUR TEAM / WORKSPACE

Drafting workspace

All recordsIn reviewCompleted
Source documents / attachedDetails and supporting information
Generated draft / needs reviewAssigned owner and next action
Reviewed version / readyRecorded status and activity
Changes stay traceableView activity ↗
Designed for the in-between, too.Loading-state concept
Illustrative UI prototype · Sample content, not a client project
05 / Fine-tuning & evaluation

Improve a measured task, not an undefined promise.

For use cases where a baseline model, prompts and retrieval do not meet an agreed need. We assess the data and test whether additional training is justified.

What we can build for you

  • Representative test sets and baseline comparison
  • Training-data suitability and preparation review
  • Evaluation of quality, limitations and maintenance needs
OVERVIEW / SAMPLE WORKSPACE

Model evaluation

Baseline responses24Sample records
Review dataset08Sample records
Candidate comparison03Sample records
Activity at a glanceIllustrative data
Filters · Drill-down · ExportsOpen report ↗
Designed for the in-between, too.Loading-state concept
Illustrative UI prototype · Sample content, not a client project
06 / Tool-connected AI assistants

Let the assistant help with actions—with boundaries.

For workflows where the model needs approved access to business tools. We define what it may read, what requires permission and when a person must confirm an action.

What we can build for you

  • Scoped integrations with supported business APIs
  • Approval gates before consequential actions
  • Execution logs, failure handling and escalation
CONNECTED WORKFLOW

Controlled assistant actions

Follow the information from input to outcome.

01 / INPUTUser request & permitted context
02 / PROCESSPropose action → human approval
03 / OUTPUTExecute allowed tool → log outcome
Access controls · Review steps · Activity logs
Designed for the in-between, too.Loading-state concept
Illustrative workflow · Sample content, not a client project
A practical delivery plan

Start focused. Build with visibility.

Define the use case

Agree users, sources, limits and a representative test set.

Prepare the knowledge

Review document quality, access rules and update ownership.

Build & evaluate

Test retrieval and responses against expected answers and failure cases.

Roll out thoughtfully

Train users, monitor usage and improve with reviewed feedback.

Before we begin

Clear answers. Realistic expectations.

Is a private model always required?

No. We compare hosted and local options against confidentiality needs, model capability, infrastructure and cost. The choice is made during discovery.

Can AI give incorrect answers?

Yes. Source retrieval and evaluation can reduce errors, but do not eliminate them. Important outputs need appropriate human review and clear escalation.

Can the assistant use our company documents?

Yes, subject to your permission and the source format. We plan access controls, indexing, updates and deletion so the knowledge stays manageable.

Do we need fine-tuning?

Not necessarily. Good prompts, retrieval and workflow design may be sufficient. We evaluate a baseline before recommending additional training.