AI that does real work, not just demos.

We build agents that take on a specific, repetitive job (sorting, drafting, checking, updating) inside the tools you already use, and pass anything unusual to a person.

Talk to us about your project See how it works Call to connect+1 (214) 644-0566

Runs on your own dataEvery action is loggedPeople stay in control

operations-agentlive view

Why it matters

Most teams don’t need a general-purpose AI. They need one tedious job taken off their plate, done reliably, every day, with someone able to check the work.

Before and after

The same work, minus the busywork.

Drag across to see what changes when an agent handles the routine part of a workflow.

Done by hand

Copying data between systemsSomeone retypes emails and forms into the CRM or ERP.
Every case gets the same attentionSimple and complex requests wait in the same queue.
Knowledge lives in people’s headsAnswers depend on who happens to be available.
Hard to see what happenedWork is scattered across inboxes and spreadsheets.

With Pico Labs

Data moves on its ownThe agent reads, extracts and updates records for you.
People focus on the exceptionsRoutine cases are handled; unusual ones are flagged.
Answers come from your documentsConsistent responses grounded in your own sources.
A clear log of every actionYou can see what was done, when and why.

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What we build

Six ways we put AI to work.

Task agents

Agents that complete a defined job end to end (triaging tickets, processing invoices, preparing reports) and stop to ask when unsure.

Agentic workflowsTool use

Workflow automation

Connect the steps between your systems so information moves without anyone copying and pasting.

IntegrationsTriggersApprovals

Document processing

Pull the fields you need out of PDFs, scans and emails, and check them against your records.

ExtractionValidation

Knowledge assistants

Search and answer across policies, manuals and past cases, with links back to the source.

RAGCitations

Generative content

Drafts for replies, product descriptions and summaries in your tone, ready for a quick review.

Generative AITemplates

AI in your product

Add AI features to your existing app or platform through clean, well-tested APIs.

LLM APIsEvaluation

How it works

From a messy process to a dependable agent.

01
Pick the right job

We map one workflow with your team and find the part that is repetitive, frequent and easy to check. That’s where AI pays off first.
- Process mapping
- Feasibility
02
Prove it on real data

A working prototype runs on a sample of your actual cases, so you can judge the quality before anything goes live.
- Prototype
- Accuracy review
03
Build it properly

We connect the agent to your systems with permissions, logging and a clear hand-off to people for anything outside its rules.
- Integrations
- Guardrails
04
Measure and improve

After launch we track what it handles, where it hesitates and what people correct, and keep tuning from there.
- Monitoring
- Feedback loop

Prefer to talk it through?

Call our team and speak with the people who would actually build it.

Talk to our team+1 (214) 644-0566

Where it fits

Where agents earn their place.

Finance

Invoice matching

Read supplier invoices, match them to purchase orders and flag mismatches for review.

For exampleAn agent clears the routine matches each morning; finance only sees the exceptions.

Support

Ticket triage

Classify incoming tickets, set priority, fill in missing details and route to the right team.

For exampleUrgent outage reports go straight to engineering, with logs attached.

Healthcare

Record summaries

Summarise visit notes and forms into a structured record for staff to confirm.

For exampleField visit notes become a tidy summary a supervisor approves in seconds.

Operations

Daily reporting

Collect figures from several systems and write the morning report automatically.

For exampleManagers get one clear summary instead of five exports.

Sales

CRM hygiene

Keep records up to date from emails and calls, and spot deals that have gone quiet.

For exampleStale opportunities are flagged to the owner every Monday.

Tools & technology

Models and tools we work with.

We pick the model for the job, hosted or self-hosted, and keep your data where it needs to stay.

OpenAI

Claude

Gemini

Llama

Mistral

Ollama

vLLM

LangGraph

CrewAI

Python

FastAPI

PyTorch

PostgreSQL

Redis

Docker

Ready-made starting points

Start with an assistant.

If your first step is conversational, one of our four bots is a faster way in, each one runs on the same foundation as a custom agent.

Customer supportChat Bot

Answers customer questions from your own content, day and night, and hands over to a person cleanly.

Explore

Lead qualificationSales Bot

Asks the qualifying questions, scores the lead and books the meeting straight into your calendar.

Explore

Internal knowledgeQuery Bot

Plain-language answers from your documents and databases, always with the source attached.

Explore

Feedback & researchSurvey Bot

Conversational surveys that adapt to each answer, so more people finish and you learn why.

Explore

What you get

A working agent, not a slide deck.

Everything you need to run it, check it and change it later.

A mapped workflowWhat the agent does, what it doesn’t, and where people step in.

A tested prototypeResults on your real data before full build.

Production integrationConnected to your systems with proper permissions.

Action logsA readable record of everything the agent did.

Human hand-offClear rules for when and how it asks for help.

Ongoing tuningRegular reviews to improve accuracy over time.

FAQ

Common questions.

Something else on your mind? Ask us directly. A real person will reply.

Ask a question

Is our data used to train public models?

No. We use providers and settings that keep your data out of model training, and we can run open models on your own infrastructure when that’s required.

What if the AI gets something wrong?

We design for that. Agents work within clear rules, log every action and hand uncertain cases to a person. Higher-risk steps can always require approval.

Do we need a lot of data to start?

Usually not. Most agents use your existing documents and records rather than training a new model, so a sample of real cases is enough to begin.

Which AI model do you use?

Whichever fits the job, budget and data rules, OpenAI, Claude, Gemini or open models like Llama and Mistral. We can switch later without rebuilding everything.

How do we know it’s worth it?

We agree on what success looks like before building (time saved, cases handled, errors reduced) and test against it on real data first.

Keep exploring