Development solutions
AI agent development and business automation
Most AI projects stall between a convincing demonstration and a system the business will actually rely on. The gap is rarely the model. It is permissions, data boundaries, error handling, cost control and knowing who is accountable when the output is wrong.
Sardo Group builds AI agents and automation into existing operations: work that is high volume, text or data heavy, and governed by rules that can be stated. Each implementation is scoped to a defined process, integrated with your systems, and instrumented so you can see what it did and why.
At a glance
- Engagement
- Phased delivery
- First release
- Typically 3 to 5 months
- Relevant sectors
- Enterprise & SaaSRetail & MarketplacesConsumer Electronics
Where this work begins
The situations that bring organisations here.
These are the problems a development engagement usually starts from, and what they cost if they are left alone.
Manual workload at volume
Teams spend their day reading documents, re-keying information and routing requests that follow predictable patterns.
Pilots that never ship
A demonstration that impressed in a workshop but cannot be connected to real systems, real permissions or real accountability.
Unsanctioned tool use
Staff pasting commercial or customer data into public AI tools because no approved alternative exists.
Automation without an audit trail
Automated recommendations with no record of the input, the model, the version or who approved the outcome.
What we develop
The systems we build.
Enquiry and intake agents
Inbound enquiries, applications and requests classified against your criteria, summarised and routed with a recommended next action.
Document and data processing
Invoices, specifications, contracts and reports read automatically, with extracted fields validated before they reach operational systems.
Workflow agents
Multi-step internal processes where the agent retrieves context, drafts or decides within defined limits, and escalates exceptions.
Assistive tools inside your platform
Search, summarisation, drafting and recommendation features added to the software your team already uses.
Capabilities
- AI opportunity assessment
- AI agent design and development
- Retrieval over your own documents
- Structured data extraction
- Classification and triage
- CRM and ERP connected agents
- Workflow and approval automation
- Human-in-the-loop review design
- Prompt and model version control
- Evaluation and regression testing
- Cost and usage monitoring
- Data boundary and retention controls
Functionality and integrations
What the delivered system typically includes.
Scope is agreed against your operating requirement. This is the range from which a first release is defined.
Retrieval and grounding
Agents answer from your documents, policies and records, with citations back to the source rather than unverifiable output.
Tool use and actions
Controlled access to create records, update status, draft correspondence or call internal services, within defined limits.
Review and escalation
Thresholds that decide what the agent handles and what goes to a person, with the reasoning available to the reviewer.
Observability
Logging of every run, input, output, latency and cost, so quality and spend can be reviewed rather than assumed.
Typical integrations
- CRM and service desk platforms
- ERP and finance systems
- Document management and storage
- Email and messaging channels
- Internal APIs and databases
- Identity and access management
- Telephony and contact centre systems
- Business intelligence and reporting
Delivery
How the engagement runs.
Every engagement is phased so something usable exists early, and cost and scope stay visible.
- 01
Process and feasibility
We identify the work worth automating, measure the current volume and cost, and state clearly where AI would not help.
- 02
Narrow first release
One process, one workflow, one measurable outcome, running against real data with human review in place.
- 03
Evaluation and hardening
A test set of real cases, measured accuracy, failure handling and the boundary between automated and human decisions.
- 04
Rollout and monitoring
Wider deployment with usage, cost and quality monitoring, and a defined path for adding the next process.
Business benefits
What changes commercially.
Time returned to judgement work
Repetitive reading, checking and routing handled by the system, with people focused on exceptions and decisions.
Faster response times
Enquiries and documents processed in minutes rather than queueing for attention.
Consistent handling
Every case follows the same defined rules, and exceptions are escalated rather than quietly missed.
Operable and defensible
Permissions, logging and review steps are part of the build, so the automation can be run and audited.
Industry examples
What this looks like in practice.
Representative scenarios rather than published client engagements.
Enquiry qualification
Commercial enquiries assessed against fit criteria, summarised for the sales team and routed with a recommended response.
Invoice and document processing
Supplier documents parsed into validated records, with mismatches flagged for review before posting to finance.
Operational decision support
Pricing, grading or allocation recommendations produced within parameters, with approval required above an agreed threshold.
Questions
Frequently asked.
Where work is high volume, text or data heavy, and rule governed. Document processing, triage, classification, summarisation and drafting are usually productive. Strategic judgement and commercial accountability are not delegated to a model.
Data handling is defined per implementation: which systems are accessed, what is sent to a model, what is retained and who can see it. Where a requirement can be met without sending sensitive data to a third-party provider, we design it that way.
Where the outcome affects a customer, a payment, an entitlement or a compliance position, yes. We define approval thresholds and escalation paths, and keep a record of the decision and who made it.
It depends on the process and the integration surface rather than the model. A single well-bounded workflow integrated with one or two systems is a defined piece of work; a multi-process deployment across several systems is a programme. We scope and price the first workflow before discussing the rest.
Against the baseline recorded before the build: volume handled, time per case, error and escalation rates, and the cost per run. If the numbers do not improve, the automation is not working, whatever the demonstration suggested.
Next step
Explore AI development
Tell us which process consumes the most manual effort and what the current volume looks like. We will say plainly whether automation is the right answer and what the first workflow would involve.