110Labs
Services/Data & Intelligence/Generative AI Solutions
Service 06Data & Intelligence

No hype. Systems that ship.

LLM-powered assistants, document intelligence and RAG knowledge systems, engineered with the error handling, cost controls and monitoring that separate a demo from production.

At a glance
PatternsRAG, agents, document intelligence
Typical engagement8–16 weeks
DeliverablesWorking system, evals, cost model
Follows on toAI & Copilot, Observability
01What we build

Six things, all in production somewhere.

Grounded answers, measured quality and a cost per request you can forecast, the parts most AI pilots skip.

01

AI assistants & chatbots

Conversational interfaces that hold context across turns and integrate with your systems: support, internal knowledge, domain copilots.

02

RAG & knowledge systems

LLMs connected to your proprietary data through vector search over documents, databases and wikis. Grounded answers instead of hallucinations.

03

Document intelligence

Extract, summarise and analyse contracts, reports and unstructured documents at scale, automating manual review workflows.

04

AI agents & workflow automation

Autonomous agents that reason, plan and execute multi-step tasks: research, report generation, API orchestration, decision support.

05

Prompt engineering & evaluation

Systematic prompt design with evaluation frameworks and guardrails, so behaviour is reliable rather than anecdotally good.

06

Integration & deployment

Production pipelines with error handling, caching, cost management and monitoring, wired into your existing applications.

02AI solution stack

Five layers, and most projects fail at the bottom two.

Retrieval quality and data access decide whether the top of the stack is useful. Model choice is rarely the constraint.

ApplicationsAssistants, copilots and the interfaces people actually touchLayer 05
OrchestrationAgents, tool use, evaluation harnesses and guardrailsLayer 04
AI platformsModel selection, routing, fallback and cost controlLayer 03
Data & retrievalEmbeddings, vector search and the knowledge that grounds an answerLayer 02
InfrastructureHosting, pipelines, access control and auditLayer 01
03Our approach

Prove it small, then engineer it.

The order matters: an evaluation set before a prototype means you can tell whether iteration is helping.

Phase 01

Frame

Pick a workflow with a measurable outcome, and write down what good looks like.

Phase 02

Evaluate

Build the evaluation set first, so quality is a number rather than a feeling.

Phase 03

Build

Working system against real data, with guardrails and cost ceilings from the start.

Phase 04

Operate

Monitoring on quality and spend, and a retraining or re-prompting cadence.

04Real-world use cases

Where it has paid off.

Patterns we have shipped. Each one started as a single workflow with a number attached to it.

Customer support automation with context-aware responses
Internal knowledge base search across company documents
Automated report generation from structured and unstructured data
Contract analysis and clause extraction at scale
Code review assistants and developer productivity tools
Content generation pipelines with brand voice consistency
Data enrichment and classification using LLM reasoning

Ready to explore AI for your business?

Bring one workflow that costs your team hours a week. We will tell you honestly whether AI is the right tool for it.

Scope an AI project