Custom AI agents, built into the software you already run.
Enterprise AI agents and custom AI solutions designed around how your team actually works. They run inside your CRM, ERP, inbox and internal tools, and take a workflow from intake to action to reporting. Measured against your baseline, with people approving what matters.
- No migration
- Tested on your real cases
- Human approval built in
One agent system, the whole workflow
- 01
Intake
- 02
Understand
- 03
Decide
- 04
Act in your systems
- 05
Report
Orders, invoices, reports, approvals, onboarding and internal requests, handled end to end. People step in on exceptions and anything that needs judgment.
Most companies are adding AI one tool at a time. Each one does a piece of the job. Your team is still the one connecting them.
Dapto connects the process, not just the tools.
Lead generation
A sales tool finds leads.
Sales
An outreach tool sends messages.
Customer service
A chatbot answers questions.
CRM
A CRM stores customer information.
Operations
An automation tool moves data.
What fills the gaps today
- Manual handoffs
- Copy and paste
- Missed follow-ups
- Spreadsheets
- Slack messages
- Someone chasing it all
An agent that answers one inbox isn't transformation. We build agent systems that handle a workflow from start to finish, from intake to action to reporting, so whole chains of manual work disappear.
We map the whole process first
- 1
Where does the work start?
A form, a call, a signal
- 2
What information is needed?
CRM, orders, policies
- 3
What decisions get made?
Qualify, route, refund
- 4
Which systems are involved?
Inbox, calendar, help desk
- 5
Where does a person step in?
Approvals and exceptions
Most AI projects stall before production. Ours follow the practices that get them there: start from the workflow, measure a baseline, test on real cases, roll out in stages and keep improving. The same four steps apply to any workflow, prebuilt or custom.
- 1Discover
AI Opportunity Audit
We study how the work really happens and measure it, so we automate the right thing and can prove the result.
- Map the workflow end to end: people, systems, decisions and handoffs
- Measure today's baseline: volume, time, cost and error rate
- Decide which steps need an agent, and which need simple automation or should stay with a person
- Rank opportunities by value and risk, then agree the first build and its success target
Your partSessions with the people who do the work, and read-only access to the systems involved.OutputAI Opportunity MapAI Opportunity Map
Illustrative example: order intake at a 120-person distributor
We found 254 hours a month of manual work. Order intake is the biggest, so we start there.
- Order intake from emailed POsStart here120 hrs/month
- Supplier invoice matching80 hrs/month
- Quote follow-ups30 hrs/month
- Weekly sales reporting24 hrs/month
Order intake today
Every order retyped by hand
6 min each · 4% errors
Target
No retyping
95%+ entered correctly
- 2Design and build
Workflow design and build
We redesign the workflow around the agent and prove it works on your real cases before it touches live work.
- Redesign the workflow with a clear human role at every step
- Give each agent a job description: its tasks, tools, permissions and your Brand Core
- Build a test set from your real past cases, with your experts defining what good looks like
- Reuse our prebuilt agents and components, and build only what is unique to you
Your partYour experts review the test cases and approve the business rules.OutputAI Workflow ArchitectureAI Workflow Architecture
Illustrative example: order intake at a 120-person distributor
The agent handles 4 of the 5 steps. Your team only steps in when something doesn't match.
Agent
Your team
- 1Picks up the order email
- 2Reads the items, quantities and prices
- 3Checks stock, prices and credit in the ERP
- 4Approves it, only if something doesn't match
- 5Creates the order and confirms to the customer
Tested on 300 past orders
Ready to launch97% correctTarget 95% - 3Deploy
Production deployment
The agent goes live inside your existing systems in stages, so nothing breaks and trust is earned.
- Connect to your systems with only the access the agent needs. No migration
- Run in shadow mode first, side by side with your team
- Go live on a small share of the work, then expand once the targets hold
- Keep human approval on risky actions, with a rollback plan ready
Your partApprove each stage of the rollout. Your team gets a short handover.OutputProduction AI SystemProduction AI System
Illustrative example: order intake at a 120-person distributor
Live in three safe stages. Your team approved each one before the next.
Shadow
0%
of orders handled by the agent
Agent: Prepares orders
Team: Still enters every order
Matched 96%
Pilot
20%
of orders handled by the agent
Agent: Enters 1 in 5 orders
Team: Approves each one
No errors in 2 weeks
Live
100%
of orders handled by the agent
Agent: Enters every order
Team: Checks exceptions only
Live
- Only the ERP access it needs
- Mismatches always go to a person
- Rollback plan for every stage
- 4Ongoing
Ongoing optimisation
We run and improve the system, and measure it against where you started.
- Monitor every step of every run, not just the end result
- Review exceptions and corrections, update the agent and re-test before every change
- Report results against the baseline every month
- Give agents more autonomy where they have earned it, and reuse them for the next workflow
Your partA monthly review of results and priorities.OutputManaged AI OperationsManaged AI Operations
Illustrative example: order intake at a 120-person distributor
Order entry went from 120 to 18 hours a month, with fewer mistakes.
BeforeAfterHours a month on order entry
12018Orders entered with errors
4%0.8%Orders with no human touch
0%82%
Next: supplier invoices, reusing the same agent
Step 1 output: AI Opportunity Map
AI Opportunity Map
Illustrative example: order intake at a 120-person distributor
We found 254 hours a month of manual work. Order intake is the biggest, so we start there.
- Order intake from emailed POsStart here120 hrs/month
- Supplier invoice matching80 hrs/month
- Quote follow-ups30 hrs/month
- Weekly sales reporting24 hrs/month
Order intake today
Every order retyped by hand
6 min each · 4% errors
Target
No retyping
95%+ entered correctly
A working session on your actual workflows.
Start with the business problem. We work out which agents solve it, what should stay with your team and what to build first, before you invest in anything.
Book a CallYou leave with a clearer picture of where AI can help, what should stay with people and what to build first.
What we look at
- Your workflows
- Manual handoffs
- Customer journey
- Systems
- Data
- Decision points
- Repetitive work
- Revenue leakage
- Operational bottlenecks
Replacing your CRM, help desk, ERP or email platform is usually unnecessary. Dapto sits between the tools your team already uses, does the work across them, and hands people the decisions.
Your systems
- CRM
- Website
- Phone
- Calendar
- Help desk
- ERP
- Documents
- Databases
- APIs
Dapto
The operations layer that does the work
- Agents
- Knowledge
- Orchestration
- Business rules
- Security
- Evaluation
Sales
Takes the meetings
Support
Handles the exceptions
Operations
Approves what matters
AI built around your business, not the other way around.
End-to-end workflows
We look past the single task and automate the whole process wherever it makes sense.
Built around your systems
We connect to the tools and data your business already uses. Nothing to migrate.
People stay where they matter
Agents work on their own where it is safe and hand decisions to a person when judgment is needed.
Designed for production
Monitoring, evaluation, permissions, knowledge management and security are part of the build, not an add-on.
AI Governance
Guardrails on every agent we deploy
Agents only touch what they are allowed to, and a person approves anything that matters. Built into every deployment, with stricter controls for enterprise teams.
Available for enterprise deployments- Control what each agent can see and do
- Set policies per workflow
- Log and monitor every action
- Evaluate outputs before they ship
- Require approval for sensitive steps
- Protect sensitive information
Common questions about custom AI agent projects.
What is AI agent development?
AI agent development is designing, building and deploying AI agents that do real work inside a business: reading documents, updating systems, answering customers and handing decisions to people. Dapto builds custom AI agent systems that run inside your existing software, starting from prebuilt agents where they fit, and tests them on your real cases before they go live.
What are enterprise AI agents?
Enterprise AI agents are AI agents that run inside a company's own systems, such as its CRM, ERP, inbox and internal tools, and carry out whole workflows with the access, approvals and logging a business needs. Dapto builds them as custom AI solutions around how each team works: every agent gets only the permissions it needs, sensitive steps go to a person, and every action is logged.
What happens in the AI Opportunity Audit?
It is a working session, not a sales pitch. We go through how your work runs today, measure where time and money are lost, identify where an agent could realistically take over, which systems it would need to connect to, and what to build first.
How long does it take to build an AI agent?
It depends on the workflow and the systems involved. Because most builds start from agents and components we have already built, they move faster than a build from scratch. The audit gives you a clear plan before anything is built.
Do we need to change our software?
No. Agents connect to the systems you already use through their APIs and integrations, within the permissions you set. Nothing is migrated or replaced.
How do you make sure the agent is reliable?
We build a test set from your real past cases and agree a success target before go-live. The agent runs in shadow mode first, then on a small share of the work, and only expands once the targets hold.
How much of my team's time does this take?
Your team gives us context on the workflow and access to the right systems in a few working sessions, then reviews test results and approves each stage of the rollout. We handle the design, build and deployment.
What if we already know what we want to automate?
Good. The audit then confirms the baseline and success target for that workflow, and we move straight into design.
What does custom AI agent development cost?
Pricing depends on the workflows, systems and number of agents involved. We share it after the AI Opportunity Audit, once the scope and the expected result are clear.
Book an AI Opportunity Audit and we will map where agents can take over first, with a baseline to measure against.
- 1Start with one painful workflow
- 2Prove the value
- 3Then expand