Custom AI Agents
We build AI agents that actually do the work — not demos that break in week two.
Agents that read your own documents before they answer, work inside the tools your team already uses, and check with a person before anything that cannot be undone. You own the build. We prove it works on one job before you roll it out.
Trusted by founders and leaders at 185+ growing companies
K Charles
McIntyre Inc
Reporting Hub
Suntiff
REHQ
Cadre AI
ViB Tech
Good2beSocial
Get Trained AI
Hustle20
Collectiv Academy
Easy Street Offers
Gobel Group
Digital Diabetes
LearnBrands
PureNSM
Uncommon Agency
Euka AI
Alphaline Tiny Homes
BetterFleet
Garner Bio
Sports Plus Team
Joshua Trees Experts
Nueways Studio
All State Game
Digital Momentum
Compound Fitness
One Base Health
Pop Events Co
Bayaan Academy
Hemlane
Farotech
TechForGood
Naked Pools
Rocket SaaS
Crown Workforce Management
Barcliff Group
Hermetic AI
Performativ
Beeontrade
JCI InstituteK CharlesMcIntyre IncReporting HubSuntiffREHQCadre AIViB TechGood2beSocialGet Trained AIHustle20Collectiv AcademyEasy Street OffersGobel GroupDigital DiabetesLearnBrandsPureNSMUncommon AgencyEuka AIAlphaline Tiny HomesBetterFleetGarner BioSports Plus TeamJoshua Trees ExpertsNueways StudioAll State GameDigital MomentumCompound FitnessOne Base HealthPop Events CoBayaan AcademyHemlaneFarotechTechForGoodNaked PoolsRocket SaaSCrown Workforce ManagementBarcliff GroupHermetic AIPerformativBeeontradeJCI InstituteStart here
You do not need an AI strategy. You need one job off your plate.
The businesses that get real value out of this never start with the technology. They start with a job that is quietly costing them: something repetitive, something that stalls until a person reads it, something that only gets faster if you hire. If any of the following sounds like your week, there is probably an agent worth building.
Someone retypes the same information twice. It comes in on one system and gets keyed into another, every day, by hand.
Work waits overnight for one person. Nothing moves until somebody opens it, reads it and decides where it goes.
Quotes and reports take days. Not because the decision is hard, but because gathering everything takes so long.
The same questions come back every week. Your team answers from memory, or goes hunting for the right document.
Your current automation breaks on anything unusual. It handles the standard case and drops everything else on a person.
The only way to handle more is to hire. Volume goes up, headcount goes up, and the margin never moves.
How we are different
We ship a working system you own.
It reads before it answers
Every answer comes from your own documents and records, and shows you where it came from. When it is not sure, it hands the question to a person instead of guessing.
It works inside your tools
It gets its own limited access to each system, with only the permissions it needs, and does the work where your team already works.
You own all of it
The build, the instructions, the data and the choice of AI model behind it. Nothing is locked to one supplier, including us.
Proven on one job first
We agree the number it has to move, build it for one job, and show you the result. You decide from there whether it goes wider.
What we build
One agent, built around one workflow that is costing you.
Onboarding agents
Walk a new client or new starter through every step, chase the missing document, and tells a human when something stalls.
Client & staff onboarding
Knowledge assistants
Answer your team's questions from your own handbooks, policies and product docs, and show the page it came from so anyone can check it.
Support & ops / HR
RevOps & CRM agents
Scored the new lead, fills in what is missing, send it to the right person and log it all in the CRM. No manual typing.
Sales & marketing
Voice agents
Answers and makes calls, asks the qualifying questions, books the slot and writes it all back to your CRM.
Bookings & front desk
Document processing agents
Reads invoices, contracts, purchase orders and forms, pulls out the details you need, and files them where they belong.
Finance & admin
Quoting & estimating agents
Turns a rough enquiry into a priced, formatted quote using your exact rules and rates, ready for someone to approve and send.
Sales & finance
Inbox triage agents
Reads a shared inbox, works out what each message is about, drafts the reply and sends the odd ones to a person.
Shared accounts
Research & enrichment agents
Builds a cheat brief on a company or contact from public sources and your own history, ready in the CRM before the call.
Outbound & BDRs
Data hygiene agents
Finds duplicates, fixes formatting, fills the gaps and flags the records a person needs to look at. Ongoing, not a one-off tidy-up.
CRM & database
Reporting agents
Pulls the weekly numbers together, writes the summary in plain English, and posts it with anything unusual called out.
Leadership & ops
Scheduling & dispatch agents
Matches jobs to the right person and time slot based on skills, location and availability, then handles the reschedules.
Field service
Multi-agent systems
Several agents working together on long jobs with many steps, like checking two systems match after a migration.
Complex workflows
Worked example - quoting agent
A rough enquiry in. A priced quote out.
One of the agents we build most often. It turns a scrappy message into a properly priced, formatted quote using your own rates and your own rules, ready for a person to check and send.
As it arrived
SUBJECT: QUICK PRICE?
No part numbers. No quantities in a form. Half the detail is "same as last time."
Reads the enquiry
No part numbers. No quantities in a form. Half the detail is "same as last time."
Finds your rates
From your own price list, not a guess
Applies your rules
Rush loading, site travel, repeat-client terms
Builds the document
Your template, your wording, your numbers
Under a minute, start to finish
What lands for approval
A person opens it, checks it, hits send. That step stays human on purpose.
Illustrative figures. Steps two and three are the part with a name: RAG, or retrieval-augmented generation, where the agent answers from your own rate card and terms rather than from memory. Step four runs over MCP, an open standard for connecting a model to your tools, which is what lets you change the model later without rebuilding anything.
Under the hood
The engineering underneath is what decides whether it works.
One of the agents we build most often. It turns a scrappy message into a properly priced, formatted quote using your own rates and your own rules, ready for a person to check and send.
Retrieval
It looks things up first
Before answering, the agent finds the relevant lines in your own documents and records, then answers only from those and shows you the source. The industry name for this is RAG, or retrieval-augmented generation, and it is the main reason a well-built agent does not invent things.
RAG - vector stores - hybrid search - re-ranking - enforced citations
Connection
It plugs into your systems
We connect the agent to your tools using an open standard called MCP, the Model Context Protocol. Because it is a standard rather than our own invention, swapping the AI model behind it later is a settings change instead of rebuilding every connection.
MCP servers - typed schemas - scoped credentials - least privilege
Control
It has rules it cannot break
There is a list of things the agent is allowed to do and a person has to approve anything that cannot be undone. Personal details are stripped where they are not needed, and it is built to resist being talked into ignoring its instructions.
Guardrails - action allow-lists - PII redaction - human-in-the-loop
Quality
It gets marked, like an exam
We build a test set from your real cases and score the agent against it before launch and after every change. You get a pass rate rather than someone's opinion, and anything that breaks gets caught before your customers see it.
Evals - golden datasets - adversarial cases - regression runs
Orchestration
Fixed steps where fixed steps work
Most jobs do not need AI for every step. Where the path is always the same we use ordinary automation, and save the AI for the parts that genuinely need judgement. That makes it cheaper to run, faster, and far easier to fix.
Fixed workflow steps - state machines - scoped agent loops
Observability
You can see what it did
Every lookup, action and decision is recorded and readable. When someone asks why the agent did that, you can show them, step by step. Running costs are tracked in the same place, so there are no surprise bills.
Observability - full traces - alerting - cost tracking - replay
Proof
We have shipped 160+ implementations. Here is what a working system actually changes.
Quoting workflow, before and after
Before
Enquiry sat in a shared inbox. Someone re-keyed it, hunted the rate card, built the quote by hand. Days, not hours.
After
Enquiry parsed on arrival, priced against the rate card, quote drafted and queued for one human approval.
Lead handling, before and after
Before
Inbound leads sat overnight. Manual enrichment and routing. CRM fields half-filled and unreliable.
After
Qualified, enriched and routed on arrival. Clean records. The rep picks up a warm, complete deal.
These are representative of the workflows we build, not client-attributed claims. The workflows that show a result fastest are the ones where a baseline already exists, because you are already measuring the number the agent is meant to move. We will model your specific figures on the call.
The process
We prove it in one workflow before you bet the budget.
1-2 weeks
Discovery & scoping
We map the workflow, audit your data, define the success metric, and agree guardrails and ownership. Skipping this phase is why projects run triple over.
2-6 weeks
Pilot build
We build it against your real data for one job, with a person approving the output, and score it against a test set from day one.
to ~12 weeks
Production hardening
Alerts, fallback paths for when it is unsure, full activity records, security controls, and automatic checks on every change.
Ongoing
Scale & optimise
Roll it out to the next job, tune it weekly against the number we agreed, and report on it somewhere you can actually read.
A simple single-workflow agent is usually live in two to six weeks. A complex multi-system build runs eight to sixteen weeks. You get an honest phase-by-phase estimate on the call, with no sandbagging.
WHY GROWLYZE
WE'RE NOT CONSULTANTS.
WE'RE BUILDERS.
You own everything
no lock-in, fully yours.
One partner, not ten
everything under one roof.
Built to scale
grows with you, no rebuilds.
Process first, always
fix the workflows, then the tools.
FAQ
The questions we hear most.
CONTACT US
LET'S FIGURE OUT
YOUR AI
Our Platform Partners
Every platform we build in has vetted us. None of this is self-declared.
