Copilots & agents

A dedicated assistant for every team, trained on your data

We build enterprise copilots and AI agents that read your documents, know your processes and work next to your people. Not demos: products actually used every day by sales, support, legal, HR and engineering.

4-8 wks

To release the first copilot to internal production with a pilot group

-50%

Average time to find the right answer in large internal knowledge bases

100%

Answer traceability with citations back to original sources

Custom LLMs, not generic chatbots

A public model does not know your price list, framework contracts, tone of voice or internal policies. Our copilots do. They start from frontier models (Claude, GPT, Llama, Mistral) and we specialise them on your processes with retrieval, fine-tuning and bespoke guardrails. When needed they turn into agents: they execute actions through tools and APIs, always with human controls.

RAG on real data, not playgrounds

We connect the copilot to SharePoint, Drive, Confluence, databases, tickets, contracts. Answers cite the source and respect the original permissions of every document.

Agents with tools, not talkative chatbots

To automate work we build agents that use APIs, SQL queries, compute engines, RPA. They run concrete actions and stop when their confidence drops below a threshold.

Measured quality, not hoped-for quality

Every copilot has a golden question set, automated evals, accuracy and cost monitoring. If a release worsens metrics, we roll back automatically.

Live snippet

An agent that answers citing its sources

Real example: an agent with tool use that searches internal documents, cites its sources and escalates to human review only when confidence drops below threshold.

python@lbd studio/ai.snippet

                

The copilots we build

Different departments need different assistants: we design specialised copilots, not a single generic bot.

Sales

Sales copilot

Summarises calls, drafts proposals, answers RFPs, suggests next best actions and keeps the CRM up to date for sales reps.

  • Integrated with Salesforce, HubSpot, Dynamics
  • Proposal drafting from templates and case history
  • Automatic lead scoring with explanations
Support

Support copilot

Sits next to agents suggesting answers based on your manuals and history, and answers autonomously where possible with auto-escalation.

  • Smart triage for incoming tickets
  • Answers with citations to internal KB
  • Agent assist inside Zendesk, Freshdesk, Teams
Knowledge

Internal knowledge assistant

A single place where anyone can ask about procedures, policies, tutorials, technical specs — in natural language, respecting permissions.

  • Indexes documents, wikis, emails and chats
  • Multilingual semantic search
  • Document-level permissions honoured
Legal & Compliance

Contract copilot

Contract review, critical clause extraction, version comparison, alerts on deviations from standard clauses.

  • Tailored review playbooks
  • Assisted redlining with changelog
  • AI Act, GDPR and internal policy compliance
HR & People

People copilot

Assistant for onboarding, leave requests, company policies, internal skills search, job description and review drafting.

  • Multi-step automated onboarding
  • Internal talent matching
  • Sensitive conversations kept private
Engineering & IT

Internal tech copilot

Stands next to developers with code search, documentation, runbooks and dev tools. Integrated with GitHub, Jira, PagerDuty.

  • Semantic multi-repo code search
  • Interactive operational runbooks
  • On-call assistance during incidents

How we build a copilot

Three clear stages, each with objective criteria to decide whether to continue.

01

1. Use case discovery

We understand who will use the copilot, which questions they ask, where answers live today. We write 30-50 representative questions that will benchmark quality over time.

Output: golden set, source map, security and permissions architecture.

02

2. Prototype with retrieval and guardrails

We build the first version on Claude/GPT, a RAG engine over your documents and guardrails for prompt injection, PII and forbidden topics. We iterate on the golden set until metrics are good.

Output: internal copilot, eval dashboard, governance and logging.

03

3. Production, agents and scale

We go live with a pilot group, extend across the department, add agents that execute actions (book, update, send). We evolve models and knowledge over time.

Output: operating copilot or agent, SLA, quarterly evolution plan.

Models, frameworks and platforms

We pick the right mix of quality, cost, privacy and latency together with you.

Frontier models

Claude (Anthropic), GPT (OpenAI), Gemini (Google), Azure OpenAI, AWS Bedrock

Self-hosted open source

Llama, Mistral, Qwen, Phi, DeepSeek on dedicated GPUs or Kubernetes

RAG & retrieval

LangChain, LlamaIndex, Vespa, Qdrant, Pinecone, pgvector

Agents & orchestration

LangGraph, CrewAI, Temporal, MCP (Model Context Protocol)

Eval & safety

Promptfoo, Braintrust, Ragas, Lakera Guard, custom policies

Interfaces

Web UI, mobile, Slack, Teams, Outlook, inside CRM/ERP

Let us design your first enterprise copilot

A 45-minute session: we pick the highest-impact use case and hand you a release plan within 8 weeks.

Frequently asked questions

Questions about enterprise copilots and AI agents

How we design AI assistants that are secure, accurate and actually used by teams.

What's the difference between a copilot and an agent?
A copilot suggests: it summarises, drafts, finds information. An agent executes: it updates a CRM, opens a ticket, books a room, sends an email, runs a SQL query. We design both, with human-in-the-loop controls calibrated to the risk of each action.
How do they avoid making things up?
We use RAG: every answer comes from real documents (SharePoint, Confluence, wikis, contracts, tickets) cited verifiably. We add guardrails that refuse to answer when confidence is low or the question is out of scope. Golden tests prevent regressions.
Do they respect original document permissions?
Yes. Permissions from SharePoint, Google Drive, Confluence and other systems are propagated per user: a copilot never shows a user a document they cannot read in the original system. This rule is non-negotiable.
How do costs compare to ChatGPT Enterprise or Microsoft Copilot?
A generic catalogue copilot costs less but only knows Office. A custom copilot knows your data, your processes and integrates where you need. It typically costs 30-50% more per user but generates 3-5× the savings because it solves specific problems, not generic ones.
Can we start with one department and scale?
Yes, that is the approach we recommend. We start with sales or support where impact is most visible, validate metrics and culture, then extend to legal, HR and engineering. The base platform stays the same; sources and actions change.