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Service 01 — AI and Generative AI

Generative AI grounded in your own data.

We design and build AI applications that understand your documents, systems and processes — secure, measurable and connected to the way your organization already works.
Discuss your projectSee the architecture
Source code on a monitor viewed over the shoulder of an engineer

The business problem

Most AI initiatives stall between the demo and the daily workflow.

A general-purpose chatbot can impress in a meeting, but it rarely knows your policies, your products or your customers. Without access to trusted internal information, answers are generic at best and wrong at worst.

The harder work sits underneath: preparing knowledge sources, controlling who can see what, evaluating answer quality and connecting outputs to real systems. That is where we focus.

Signals this might be you

  • 01Staff spend hours searching shared drives, email and wikis for answers
  • 02AI pilots exist, but nobody trusts them enough to use them daily
  • 03Sensitive data cannot be sent to public AI tools
  • 04Document-heavy processes are still handled manually

What we provide

What LT Lab provides

End-to-end delivery of production AI applications, from use-case selection and data preparation to deployment, evaluation and ongoing improvement.

  1. 01

    Use-case discovery

    We identify where generative AI creates measurable value and where simpler automation is the better answer.

  2. 02

    Knowledge engineering

    Ingestion, cleaning, chunking and indexing of documents, databases and systems into a governed knowledge layer.

  3. 03

    Application development

    Assistants, copilots and AI features built into the tools your teams already use, or delivered as new interfaces.

  4. 04

    Evaluation and guardrails

    Test sets, answer-quality metrics, citation checks, access controls and monitoring before and after launch.

Capabilities

What this service covers

  • Generative AI applications
  • Enterprise knowledge assistants
  • Retrieval-Augmented Generation
  • Private knowledge bases
  • LLM integrations
  • AI search
  • Document intelligence
  • Custom copilots

Typical architecture

A typical retrieval-augmented architecture

Answers are generated from approved sources, cite where they came from and respect existing permissions.

  1. 01

    Sources

    Where organizational knowledge already lives

    • Documents
    • SharePoint / Drive
    • CRM and ERP
    • Databases
    • Email
  2. 02

    Knowledge layer

    Parsed, chunked, embedded and permission-aware

    • Ingestion
    • Embeddings
    • Vector index
    • Metadata and ACLs
  3. 03

    Reasoning

    Retrieval, ranking and model orchestration

    • Hybrid search
    • Re-ranking
    • LLM
    • Prompt templates
  4. 04

    Experience

    Where people use it

    • Web assistant
    • Teams / Slack
    • Embedded copilot
    • API
  5. 05

    Operations

    Keeping it reliable

    • Evaluation
    • Logging
    • Feedback loop
    • Cost control
Data flows through Sources, then Knowledge layer, then Reasoning, then Experience, then Operations.

Example use cases

Where this creates value

01

Internal knowledge assistant

Staff ask questions in plain language and receive answers drawn from policies, procedures and past work, with links to sources.

Outcome: Faster onboarding and fewer repeated questions to specialists

02

Document intelligence

Contracts, invoices, applications and reports are read, summarized and turned into structured data for downstream systems.

Outcome: Less manual data entry and faster document turnaround

03

Customer-facing AI search

Clients search product documentation, support content or regulations and receive precise, cited answers.

Outcome: Better self-service and lower support volume

04

Sales and proposal copilot

Draft proposals, responses and briefs using approved content, past submissions and CRM context.

Outcome: More consistent submissions in less time

Technologies

Chosen for your environment, not our preferences.

Models
OpenAI / Anthropic / Gemini / Open-weight models
Frameworks
LangChain / LlamaIndex / Custom orchestration
Retrieval
pgvector / Vector databases / Hybrid search
Delivery
Python / FastAPI / Next.js / Azure / AWS / GCP

Engagement process

How an engagement runs.

  1. 01 —

    Understand

    We map the workflows, systems, data and business objectives.

    • + Stakeholder workshops
    • + Process and system mapping
    • + Data assessment

    Problem definition and priorities

  2. 02 —

    Architect

    We design a technical approach around the organization's existing environment and future requirements.

    • + Solution architecture
    • + Technology selection
    • + Delivery plan

    Architecture and roadmap

  3. 03 —

    Build

    Our engineers develop, integrate and test the solution iteratively.

    • + Iterative sprints
    • + Integration and testing
    • + Regular demos

    Working, tested software

  4. 04 —

    Improve

    We deploy, monitor and continue optimizing the system as the organization grows.

    • + Deployment
    • + Monitoring and support
    • + Continuous improvement

    A system that keeps getting better

Related services

Start a conversation

Let's talk about AI and Generative AI.

Tell us about the problem, the systems involved and where you want to be. We'll help you work out the right first step.

Tell us about your projectconsulting@ltlabsolutions.io