AI process automation
We use AI agents to run a costly, repeatable manual process end to end: reading the inputs, doing the work, and handing people the decisions that need judgement. We build these systems in production, not in a slide deck, every one carrying a payback line.
What is a costly manual process actually costing you?
Most teams have a process that runs on people copying data between systems, checking documents by hand, triaging requests, and chasing the next step. It works, but it does not scale, it is slow, and the error rate climbs with volume. The usual fixes are to hire more people or buy another tool. Neither compounds. The work still lands on someone, and the tools rarely talk to each other.
What can AI process automation do?
Three capabilities, combined to fit your process. Each links to how we build it.
AI agents
Agents that run on your existing platform and do multi-step work end to end.
- Connected to your data and tools
- Multi-step work with guardrails
- Monitored in production
Voice agents
Customer-facing voice that handles the call and records what happened.
- Answers and resolves customer calls
- Routes what it cannot handle
- Logs the result in your systems
Document & knowledge automation
Turn paperwork and policy into checked, structured answers your team can act on.
- Reads contracts, forms and emails
- Returns checked, structured answers
- Grounded in your own policy
How does an automation build work?
Three steps, from a paid discovery to a monitored system in production.
Process Discovery
A short, paid engagement that maps the real process before anyone writes code.
- We map the process end to end
- We find the steps worth automating
- You get a fixed-price plan with the payback line
The build
We build on the stack you already run, connected to the systems you already trust.
- Agents deployed on your existing systems
- Connected to your data with proper authentication
- A human kept in the loop where judgement matters
Run it
It goes live with the checks that keep it working as the work grows.
- Evals and monitoring in place
- An SLA as volume and edge cases grow
- Money back if the proof of concept fails
Where has this worked?
We run our own company on six connected Claude agents across sales, finance and delivery. Beyond that, we have built automation across regulated and high-volume work.
Pharmacy operations
An eight-agent prescription pipeline taking a 15 to 20 minute manual check down to seconds, to a regulated standard.
Clinical research (Lumono.ai)
A nine-agent pipeline that turns a plain-English research question into publication-ready analysis with citations.
Order-to-schedule (secure logistics)
Manage-by-exception scheduling: an intake agent plus a 70-plus-rule engine, designed from a delivered discovery.
Where does AI process automation pay off?
Automation patterns proven in regulated and high-volume work. Each one links to how we build it and the case behind it.
Financial Services & Compliance
Compliance-grade file review, research and reporting
- First-pass contract and file review against your playbook
- Regulatory research and monitoring
- KYC and AML document triage
- Reconciliation and exception flagging
- Audit logging on every step
- Board and regulator-ready report drafts
- Faster turnaround
- A defensible audit trail
- Less time on routine review
Healthcare & Life Sciences
Regulated pipelines for clinical and pharmacy work
- Prescription and document checking to a regulated standard
- Clinical research synthesis with citations
- Source aggregation across systems
- Structured extraction from forms and notes
- A human in the loop on every clinical decision
- Audit logging and source provenance
- Wider coverage at the same headcount
- Checks in seconds, not minutes
- Citations on every claim
HR & Recruitment
Shortlisting, interviews and onboarding at volume
- CV screening and shortlisting
- Structured interview scoring
- Adaptive voice interviews
- Onboarding question answering
- Policy drafting and review
- Candidate and employee Q&A
- Faster shortlisting
- Consistent, structured scoring
- Hours back each week
Customer Support
Resolve, route and log, by voice and chat
- Inbound and outbound voice agents
- Ticket triage and routing
- Knowledge-base answers with sources
- Booking and payment capture
- CRM logging on every call
- Escalation when judgement is needed
- 24/7 cover with no queue
- Higher first-contact resolution
- Scale without hiring
Logistics & Operations
Manage-by-exception scheduling and operations
- Order intake from messy inputs
- Rules-engine scheduling
- Exception handling with a human in the loop
- Document and form processing
- Status tracking and alerts
- Reporting and reconciliation
- Less manual triage
- Faster scheduling
- Errors caught earlier
What you can count on
90 days
To production
Fixed price, money back if the proof of concept fails.
Fixed price
Quoted after discovery
You see the number before you commit, with a payback line.
50+
AI projects delivered
Across regulated and high-volume work.
6 agents
Run Softblues itself
Sales, finance and delivery. We use it before we sell it.
What does it cost, and when does it pay back?
Process Discovery is a fixed price, scoped up front. The build that follows is fixed price too, quoted after discovery, so you see the number before you commit. Every build carries a payback line: the price divided by the value it reclaims each month. A £20,000 agent that saves £4,000 a month pays for itself in about five months.
See your paybackAI process automation: at a glance
- What
- AI agents that run a costly manual process end to end, in production.
- For
- Mid-market teams with a repeatable, high-volume manual workflow.
- Capabilities
- AI agents, voice agents, document and knowledge automation, by industry.
- How
- Paid Process Discovery, fixed-price build, then evals, monitoring and an SLA.
- Delivery
- Production in 90 days, fixed price, money back if the proof of concept fails.
- Proof
- Softblues runs its own company on six Claude agents; 50+ AI projects delivered.
- Based in
- London-based, working with mid-market firms.
Common questions about AI process automation
What is the difference between AI process automation and RPA?
Older robotic process automation follows fixed rules and breaks when the input changes. AI agents read messy, unstructured inputs, handle exceptions, and ask a person when judgement is needed. We combine both: rules where rules are right, agents where they are not.
Where do you start?
With a paid Process Discovery. We map the real process before scoping a build, so the plan is grounded in how the work actually happens, not an assumption.
Will this replace our team?
No. It removes the repetitive work and keeps people on the decisions that need judgement. A human stays in the loop wherever the cost of a mistake is high.
Which systems can you connect to?
Most of them. Databases, document stores, CRMs, ERPs and ticketing, through native connectors or a custom integration with proper authentication and audit logging.
How long until it is live?
Production in 90 days is the standard. Discovery is short, typically one to two weeks; the build timeline is fixed in the discovery plan.
How do you keep our data secure?
Builds run with role-based access, audit logging and data handling built around ISO 27001 principles, on the platform you already trust. We are a registered partner across Anthropic, Google Cloud and Microsoft, so we meet you on your stack.
Start with a process worth automating
Map a costly manual process, scope a fixed-price build, and see the payback before you commit.