AI & Automation
AI Agent Development Services for Business Workflows
AI agents that take on the repetitive back-office work your team does by hand: reading documents, answering customer queries on WhatsApp and email, and looking up or updating records in your ERP and CRM, with a person approving anything that matters.
What an AI agent does for a business
An AI agent does multi-step work in your systems: it reads an incoming email, document or message, looks up the data it needs through tools you approve, drafts or takes the next action, and stops for a person when it is unsure or when the action changes data. Every step is logged.
Most businesses do not need an AI that runs the company. They need someone to stop re-typing the same data. Supplier invoices arrive as PDFs and email attachments and are keyed into the ERP line by line. Customers ask about order status, balances and delivery dates on WhatsApp and email all day. Staff switch between the ERP, the CRM and a spreadsheet to answer one question, and approvals wait in someone’s inbox. Each task is small, repeated hundreds of times, and needs just enough judgment that fixed rules cannot handle it. That is the work an AI agent is for.
An AI agent sits between a chatbot and classic automation. A chatbot answers questions in a conversation and stops there. RPA (robotic process automation) repeats fixed clicks and keystrokes, and breaks when a screen, a document layout or an input changes. An agent reads unstructured input such as an email, a scanned invoice or a WhatsApp message, works out what is being asked, calls only the tools you allow (an ERP lookup, a CRM update, a drafted reply) and passes the result on. When it is not sure, it stops and asks a person instead of guessing.
We connect the agent to the systems you already run, through their APIs or a secured integration layer, so it works on live data rather than an export: stock and order status from the ERP, customer history from the CRM, documents from a shared inbox. Every tool has its own permissions. Actions that change data, move money or reach a customer, such as posting an invoice, updating a credit limit or sending a reply, can wait in a review screen until a person approves, edits or rejects them. Each step the agent takes (what it read, what it decided, which tool it called and who approved) is written to an audit log you can search.
We start small on purpose. The free pilot builds an agent for one workflow you choose, as the 2 to 3 key modules that prove it (for example intake, the ERP lookup and the approval screen), on a sample of your real data. Your team sees what it gets right and where it needs a person before you commit to anything larger. The full build starts only after you approve the pilot, and the code, prompts and evaluation set transfer to you on full payment.
AI agent vs RPA and rules-based automation, at a glance
The short facts first, then how an AI agent differs from robotic process automation (RPA) and fixed rules.
- Delivery time
- There is no fixed timeframe: the schedule for your AI agent is set from your requirements in the Plan step. The free pilot of 2 to 3 key modules is ready in about 2 days, and with 1,200+ developers we have the capacity to deliver a focused system in as little as 2 weeks. Long-term support under a service level agreement (SLA) follows launch.
- Price
- Timeline Digital does not publish prices for custom software, because every project is custom. The price is set in three steps: we analyse your requirements, build a free pilot of 2 to 3 key modules, and after you approve the pilot we agree the scope, milestones and commercial terms in writing. Any change request outside the approved scope is reviewed and agreed with you before extra work begins. Running costs such as model usage and hosting sit in your own accounts and grow with volume; the Plan step lists the categories that apply.
- Free pilot
- Before you commit to the full project, we build 2 to 3 of your key modules as working software, free of charge, ready in about 2 days. Your team tests the pilot, and the full build starts only after you approve it.
- Accuracy
- Measured on a labelled test set from your own work before go-live and re-run after every change. We do not quote an accuracy figure before seeing your data.
| Factor | AI agent by Timeline Digital | RPA or rules-based automation |
|---|---|---|
| Input it can handle | Emails, scanned documents and WhatsApp messages in varied wording and layouts | Structured data, fixed screens and fixed document layouts |
| How it decides | A language model works out the next step within rules and tools you allow | Fixed if-then rules written in advance |
| Systems access | Narrow tools for your ERP, CRM and inbox, each with its own permissions | Recorded clicks and keystrokes, or rules inside one system |
| When something changes | Copes with new wording or layouts; uncertain cases stop and go to a person | Breaks or waits when a screen, layout or input changes |
| Human control | A person approves actions that change data, move money or reach a customer | Exceptions go to a person |
| Audit | Every input, decision, tool call and approval logged | Run logs of each step |
| Best when | The work needs reading and judgment across several systems | Every step is predictable and can be written down |
What our AI agent development includes
Workflow Mapping
We map one workflow step by step: the inputs, the decisions, the systems it touches and where a person must stay in charge, before choosing a model.
Document & Message Intake
The agent reads emails, attachments, PDFs, scans and WhatsApp messages and turns them into structured data with a confidence score.
ERP & CRM Tools
Controlled tools for lookups and updates in your ERP, CRM, accounting or ticketing system, each limited to the permissions it needs.
Human Approval Steps
A review screen where staff approve, edit or reject any action that changes data, moves money or contacts a customer.
Audit Trail
Every input, decision, tool call and approval logged with a timestamp, so you can see what the agent did and why.
Guardrails & Evaluation
Scope limits, prompt-injection checks, output validation and a test set of your real cases, so accuracy is measured before launch and after every change.
Who AI agents are built for
Back-office & finance teams
Supplier invoices, purchase orders and delivery notes read, matched against the ERP and queued for approval instead of keyed by hand.
Customer service on WhatsApp & email
Order status, balance and delivery questions answered from live ERP and CRM data, with a handoff to staff for anything unusual.
Sales & operations teams
Enquiries from email and web forms logged in the CRM, checked for duplicates and followed up with a drafted message a person approves.
Companies that already run an ERP or CRM
Teams that want AI to work inside the systems they already use, not another disconnected tool with its own copy of the data.
Is an AI agent the right choice?
A good fit when
- One workflow repeats many times a week and needs reading and judgment, not just fixed rules.
- The systems involved (ERP, CRM, inbox, WhatsApp) can be reached through an API or a supported integration.
- There is a clear owner who can say what a correct result looks like and approve the agent’s output.
- You can provide a sample of real past cases with known outcomes.
Consider another option when
- Every step is predictable; rules-based workflow automation is simpler to build and cheaper to run.
- You only need answers, not actions; an AI assistant is enough.
- Mistakes would be costly and no one is yet available to review the agent’s output.
Usually in a first release
- One workflow, built as the 2 to 3 key modules that prove it (for example intake, an ERP lookup and an approval screen)
- A small set of tools with narrow permissions, read-only where possible
- A labelled test set from your past cases, with accuracy measured before go-live
- An approval screen for every action that changes data or reaches a customer
- An audit log of inputs, decisions, tool calls and approvals
Outside the first release unless agreed
- Additional workflows, tools or systems beyond the approved scope
- Actions without approval, unless you later decide a step has proved reliable enough
- Changes inside your ERP or CRM beyond an integration user and its permissions
- Tuning for production volumes before the pilot is approved
Anything outside the approved scope is reviewed and agreed before work begins. See how we work.
Data, controls, responsibilities and ownership
Data migration and integrations
- We document which data goes to which model provider for each workflow, and keep personal or sensitive fields out of prompts where the task does not need them.
- Commercial model APIs offer business terms and data-retention settings that vary by provider and account type. We configure the settings you choose and record them; see security and data protection.
- Where your policy requires it, open-weight models hosted in your own cloud account or on your own servers are an option. They usually mean more hosting effort and can be less accurate on hard cases, so we compare them on your test set before you decide.
- Each tool depends on the system behind it: an ERP or CRM API, a database view or a supported integration, with an integration user your team creates and controls.
- Incoming emails, documents and messages are treated as untrusted input, so instructions hidden inside them cannot widen what the agent is allowed to do.
Roles, approvals and audit
- Narrow tool permissions
- Each tool can do one thing, with the least access it needs; read-only wherever possible.
- Human approval
- You decide which actions run on their own and which wait for a person to approve, edit or reject them.
- Confidence thresholds
- Cases below an agreed confidence level, or that fail validation against your rules, go to a person.
- Evaluation and audit
- A labelled test set re-run after every change, plus a searchable log of every run, tool call and approval.
- Spend and step limits
- Caps on model calls and steps per run, so an agent cannot loop or run up usage unnoticed.
What we need from your team
- A workflow owner who defines a correct result and signs off the pilot.
- A sample of real past cases with known outcomes, to build the test set.
- API access and an integration user for each system the agent touches.
- Reviewers who approve the agent’s output during the pilot and early rollout.
- Model provider and hosting accounts in your organization’s name.
Ownership, support and running costs
- Project code, prompts, configuration and evaluation sets transfer to you on full payment, and your data and documents are yours throughout. Third-party foundation models remain the provider’s and are used under the provider’s terms; see our IP and ownership policy.
- Model provider, hosting and storage accounts are set up in your organization’s name where possible, so usage, retention settings and terms sit between you and the provider.
- Running costs by category: model or API usage (grows with volume and input length), vector index and storage, application hosting, evaluation upkeep when your content or the model changes, and monitoring. Amounts depend on volume, model choice and hosting.
- Providers update and retire models. A maintenance and support agreement covers re-running your evaluation set and adjusting prompts when that happens.
Related reading for this decision
- AI assistants and chatbots
If the need is answering questions rather than taking actions.
- AI document processing
For high-volume document extraction, often the first module of a back-office agent.
- Workflow automation
Rules-based automation for the predictable steps around an agent.
- LLM app development: RAG, agents and what ships
A plain-language guide to agents, guardrails and evaluation in production.
- Security and data protection
How access, hosting and data retention are agreed and put in writing.
How we deliver an AI agent
Custom software built around the way your business works. Five steps, with a free pilot of 2 to 3 key modules before the full build.
Step 1: Understand
We learn how your business works.
Your requirements, workflow, challenges and goals, understood before anything is recommended.
Step 2: Plan
We design the right solution around your workflow.
Modules, workflows, roles, approvals, reports and integrations, agreed before development.
Step 3: Select Technology
Choose the right technical foundation.
Technology options matched to your users, security, budget and growth, not one fixed stack.
- Free pilot
Step 4: Pilot
Test our work before full project development.
Free. You choose 2 to 3 key modules and we build them first, so you can judge our work.
The full project starts only after you approve the pilot.
- Full project
Step 5: Build & Scale
From approved pilot to complete digital system.
Full development, testing, deployment, training and support, built to grow with you.
Guides for this decision
In-depth, practical reading for teams planning this kind of project.
- Guide · 12 min readHuman-in-the-Loop Design for AI Agents: Approvals, Confidence Thresholds, Escalation and LoggingHuman-in-the-loop design decides which agent actions need a person, how uncertain results reach one, and what is recorded. Classify actions by reversibility, set confidence thresholds by comparing review cost with error cost, define escalation paths that fail closed, and log every step so a decision can be replayed.Read the guide
- Guide · 10 min readAI Agents vs RPA vs Workflow Automation: What Each Is For and How to ChooseWorkflow automation runs fixed rules over systems that have interfaces, RPA operates the screens of software that has none, an AI step reads documents and messages, and an AI agent chooses its own steps and tools. This guide gives a decision table by process type and shows how the four combine.Read the guide
- Guide · 13 min readHow to Evaluate an AI Pilot Before Rollout: Test Sets, Metrics and a Go/No-Go ScorecardAn AI pilot is ready for rollout when it meets acceptance criteria you set in advance, on a test set built from your own data, including the hard cases. This guide covers metrics by task type, human review thresholds, failure-mode testing and a go/no-go scorecard.Read the guide
- Guide · 9 min readApproval Workflow Design: Approval Matrices, Delegation and Audit TrailsA good approval workflow routes each request to the right people by rule, enforces segregation of duties and records every decision. This guide covers approval matrices, delegation, re-approval rules, audit trails, testing and a design checklist.Read the guide
AI agent development FAQ
A chatbot answers questions in a conversation. RPA replays fixed clicks and keystrokes and breaks when a screen or document layout changes. An AI agent reads unstructured input such as an email, invoice or WhatsApp message, decides the next step within rules you set, and uses approved tools to look up or update data. Often we combine all three.
Repetitive tasks that need some reading and judgment: turning supplier invoices into ERP entries, answering order and account questions on WhatsApp and email from live data, logging enquiries in the CRM, and preparing reconciliations for review. The best first workflow has high volume, a clear definition of a good result and a named person to approve the output.
Yes, if the system can be reached through an API, a database view or a supported integration. The agent gets a small set of tools, such as look up an order or create a draft invoice, each with its own permissions. If your ERP has no API, we try a secured integration layer first and use screen automation last.
The main risks are a misread document, an action on the wrong record, instructions hidden in an incoming email (prompt injection), data reaching the wrong person, and runaway model costs. We control them with narrow tool permissions, validation against your rules, confidence thresholds, human approval for actions that change data, spending limits and an evaluation set re-run after every change.
Wherever you want one to. You decide which actions the agent may complete alone, such as a read-only lookup, and which wait for approval, such as posting an invoice or messaging a customer. Approvers see what the agent read and proposes, and can approve, edit or reject it. Many teams start with approval on everything.
Yes. Every run is logged with the input received, the data looked up, the decision made, each tool call, the output and the person who approved it, with timestamps. The log is searchable, so you can answer why an invoice was posted from the record rather than from memory, and review problems afterwards.
We build an evaluation set from your past cases with known correct outcomes, run the agent on it and read every failure. Tool calls are checked as well as final answers, and pass thresholds are agreed before the pilot. The set is re-run after every change, so a new prompt or model cannot silently make the agent worse.
Timeline Digital does not publish prices for custom software, because every project is custom. The price is set after the free pilot, once you approve it. For an agent, model usage grows with the number of steps per run, so step and spending limits are part of the design. Model usage and hosting are running costs in your own accounts.
There is no fixed timeframe: the schedule for your AI agent is set from your requirements in the Plan step. The free pilot of 2 to 3 key modules is ready in about 2 days, and with 1,200+ developers we have the capacity to deliver a focused system in as little as 2 weeks. SLA support follows launch.
You choose one workflow. We build the agent for it as the 2 to 3 key modules that prove it, such as intake, the ERP or CRM lookup and the approval screen, and run it on a sample of your real data, free of charge. Your team tests it, then decides whether to agree the final project.
The pilot measures how often the agent gets your sample cases right, shows the integration working and lets reviewers try the approval flow. It does not prove accuracy on case types missing from the sample, behavior at full volume, or running cost at scale. Those are checked in a controlled rollout after you approve the full build.
We document which data goes to which provider before building. The agent uses a provider-agnostic model layer, so it can run on a commercial model API with the retention settings you choose, where offered, or on self-hosted open models when data must stay under your control. Access is limited by role and every call is logged.
See working software before you commit
Before you commit to the full project, we build 2 to 3 of your key modules as working software, free of charge, ready in about 2 days. Your team tests the pilot, and the full build starts only after you approve it.
See how the free pilot worksUnderstand
We learn your requirements and how your organisation works today.
Select pilot modules
Together we choose 2 to 3 key modules that prove the solution.
Build the working pilot
We build those modules as real, working software, free of charge.
You test it
Your team uses the pilot. The full project starts only after you approve it.
Start a conversation
Tell us how your business works.
Describe what is slowing your team down. We will help you work out what to build, and how a free pilot lets you judge our work before the full project.
Prefer WhatsApp? Start a chatWhat happens next
You send a short brief
The problem, the people involved and any target date. A senior engineer replies within 4 business hours.
We understand your workflow
A first call about how your business works today. An NDA can be signed before you share details.
You test a free pilot
You choose 2 to 3 key modules and we build them first, so you judge real software before the full project.