Fewer manual steps
Data moves between the inbox, documents and the CRM without being copied by hand.
AI integration and implementation services, from strategy to support.
We turn one business requirement into a working AI system connected to the tools your team already uses: your CRM, inbox, documents and databases.
Why integration matters
AI only helps when
it reaches your systems.
A standalone AI tool means copying information in and out by hand. AI integration services connect the model to where the work happens, so results land in the right record without retyping.
Checked results
Outputs are tested against your own examples before the workflow goes live.
Clear exceptions
Anything uncertain goes to a named person instead of failing silently.
Services
Five services,
one delivery team.
AI strategy consulting
Define a useful starting point and the work required to deliver it.
- Workflow discovery and use-case selection
- Data and system feasibility review
- Prioritised implementation plan
You receive: a ranked list of opportunities and a scoped pilot proposal.
AI automation services
Build assistants and automated workflows around a specific task.
- Knowledge retrieval and document assistance
- Structured extraction and request routing
- Agent workflows with human approval points
You receive: a working workflow tested against your own examples.
AI integration services
Connect the information and actions a solution relies on.
- API, CRM and inbox connections
- Data preparation and field mapping
- Error handling and operational logging
You receive: connections to your existing systems, with errors logged and handled.
Custom AI development, testing and deployment
Build what off-the-shelf tools cannot, and make acceptance part of delivery.
- Custom assistants and workflows using generative AI
- Representative test cases and output checks
- Deployment, access, documentation and handover
You receive: a deployed system, its documentation and a trained team.
AI operations and human review
Support for work that continues after deployment.
- Output review and exception queues
- Issue triage and escalation
- Workflow improvements based on review findings
You receive: a review queue with agreed escalation paths and regular findings.
What we connect
Built around the tools
you already use.
Specific systems are confirmed in the feasibility review. Typical connections include:
Communication
Shared inboxes, web forms and support desks where requests arrive.
Records
CRMs, spreadsheets and databases where customer and job information lives.
Documents
Shared drives and document libraries holding contracts, forms and policies.
Examples
What AI integration
looks like in practice.
Illustrative examples. Scope and feasibility are assessed before implementation.
Sales
Keep the CRM complete without retyping.
An intake workflow reads enquiry emails and web forms, creates or updates the CRM record, assigns an owner and flags missing details before follow-up.
Operations
Turn incoming documents into checked records.
A document workflow extracts the required fields from orders, invoices or applications, checks completeness and queues anything ambiguous for a person.
Customer support
Answer routine questions from approved sources.
A support assistant drafts replies from your help content, cites the source, and routes anything it cannot answer to the right person.
Finance and admin
Reduce manual matching and chasing.
A workflow matches incoming documents to records, prepares exceptions for review and drafts reminders for a person to approve.
Typical implementation
Four steps, each
with a decision.
Every implementation follows the same route, so you always know what comes next and what it depends on.
Choose the problem.
Map the workflow, measure the time it takes today, and agree the outcome worth improving.
Check the foundations.
Review sample data, system access, dependencies and who approves what.
Test a focused pilot.
Build the smallest useful version and test it against agreed acceptance criteria.
Put it into daily use.
Integrate, document, train the team and agree the support needed afterward.
Data and security
Access agreed
before work starts.
Every engagement starts with a written agreement on what we can see and do.
- Confidentiality, permitted access and data handling are agreed in writing before any access is shared
- Work starts on representative samples, not your full data
- Access is limited to the systems the workflow needs, and is removed at handover unless support continues
- Ownership of the workflows, prompts and code we build is agreed in writing before work starts
- Specific compliance requirements, such as SOC 2 or HIPAA, are discussed before any access is shared
Questions
AI implementation
services, answered.
What is the difference between AI integration and AI automation?
Automation is the workflow itself: reading, extracting, drafting, routing. Integration connects that workflow to your systems so the results arrive where your team works. Most projects need both.
Do you build custom AI or use existing tools?
Both. Tool selection is part of the feasibility review. Where an existing product fits, we configure it; where it does not, we build a custom workflow.
Which AI models do you use?
The model is chosen per workflow based on accuracy on your test cases, cost and data-handling requirements, and is documented at handover.
How long does an implementation take?
It depends on scope and systems. The pilot timeline is set in the proposal before work begins.
Who maintains it after launch?
Your team, Formals, or both. Ongoing operations covers review, exceptions and improvements; the handover documents everything either way.
Can you work inside our existing delivery team?
Yes. Formals can add implementation capacity to a consultancy, agency or internal project.
Work with FormalsHave a use
Have a use
case in mind?
Tell us the process you want to improve. We’ll help define the implementation.
Book an AI Opportunity Review