C-10AI Agents
How much of your team's week goes to multi-step work a trained agent could run?
Research, comparison, drafting, chasing. Work that is too varied for a fixed rule and too repetitive for a person.
The short answerquotable
AI agents built by AiS LABZ are purpose-built digital operators for multi-step tasks that need context, rules and judgement: researching a supplier, preparing a meeting brief, answering staff questions from company documents or compiling a weekly report. Each agent is grounded in your own data, limited to the tools and permissions you set, and pauses for human approval on anything sensitive. We scope, build, test and maintain it.
Where agents earn their keep
The work is not hard. There is just a lot of it, and it never looks quite the same twice.
Research that eats afternoons
Prospects, suppliers, competitors, regulations. Someone opens twenty tabs and writes a summary by hand.
Questions the handbook already answers
Staff ask the same policy, process and product questions in chat because finding the answer is slower than asking.
Reports assembled by copy-paste
Numbers from four systems pasted into one document every Monday, with the same formatting fixes each time.
Meetings without a brief
Nobody had time to pull the account history, the open tickets and last quarter's numbers before the call.
Procurement by email thread
Requests, quotes and comparisons live in inboxes. Ranking options means re-reading everything.
Fixed rules that break on edge cases
Simple automations fail the moment an input looks different, and a person is pulled back in.
Knowledge locked in files
Contracts, SOPs and past proposals hold the answer, but nobody can search them properly.
Follow-ups that depend on memory
The next step after a task exists only in someone's head until it is forgotten.
How an agent works
A goal goes in. The agent plans, acts within limits and shows its work.
Human approval can be added at any step. Every run is logged.
Trigger
Request, schedule or system event
Understand the goal
Task broken into steps
Gather context
Your documents, systems, rules
Act with tools
Search, read, draft, calculate
Check confidence
Low confidence pauses for review
Human approval
On anything sensitive or external
Deliver and log
Output sent, every step recorded
C-10 · AI Agents
Give the busywork to something that never gets bored.
Research, comparison, drafting and chasing, run by an agent that shows its work and asks before it acts.
What we can build
Agents we build most often.
Each agent is scoped to one job, one set of tools and one owner. We add the second agent after the first proves itself.
- Research agents: prospects, suppliers, competitors, markets
- Internal knowledge assistants on policies, SOPs and contracts
- Procurement agents that source, compare and rank options
- Meeting-preparation agents with account history and open items
- Reporting agents that compile KPIs from several systems
- Proposal and document drafting from your past work
- Inbox triage agents that classify, summarise and route
- Data-quality agents that find and fix record errors
- Compliance checks against your own rule set
- Onboarding assistants for new staff and clients
- Monitoring agents that watch sources and alert on change
- Multi-agent workflows with hand-offs between specialists
Human where it matters
Autonomy within limits. Judgement stays with your team.
An agent that can do anything is a liability. Ours do a defined job with defined tools and ask when unsure.
- 01Tools and permissions are allow-listed, never open-ended
- 02External actions such as sending or paying require approval
- 03Low-confidence outputs pause for a person
- 04Every action is logged with its reasoning
- 05Corrections feed the next evaluation round
What this improves
What changes when an agent takes the work.
Hours back every week
Multi-step research and compilation stop landing on skilled people.
Faster, consistent output
Briefs, reports and comparisons arrive in the same format every time.
Answers from your own knowledge
Staff get cited answers from company documents instead of guesses.
Fewer dropped steps
Follow-ups and hand-offs are tracked by the system, not by memory.
Example workflow / procurement
A representative workflow, not a client case study.
The exact steps depend on your approved sources, your approval rules and the systems you use.
- 01Product requirement received from the operations team
- 02Agent searches approved supplier sources
- 03Availability, lead time and pricing collected and compared
- 04Suitable suppliers contacted with a templated request
- 05Responses collected and options ranked
- 06Purchasing team alerted with a one-page recommendation
What could you automate?
Use cases by department.
Every workflow is customised around the way your team actually operates. These are the starting points we see most.
- Lead research and enrichment
- Lead qualification and scoring
- CRM updates from email and calls
- Personalised follow-ups
- Meeting preparation briefs
- Proposal drafting
- Lead routing to the right owner
- Pipeline hygiene and reminders
- Campaign data collection
- Content workflows and approvals
- Lead hand-off to sales
- Weekly reporting
- Customer segmentation
- Review monitoring and replies
- SEO content briefs
- Ad spend anomaly alerts
- FAQ handling
- Ticket classification
- Response drafting
- Knowledge retrieval
- Escalation with context
- Appointment scheduling
- Call handling
- Post-resolution follow-up
- Data synchronisation
- Order processing
- Procurement workflows
- Internal requests
- Status tracking
- Scheduling
- Notifications
- Quality checks
- Invoice data extraction
- Payment reminders
- Expense classification
- Reconciliation preparation
- Reporting
- Approval routing
- Supplier statement matching
- Cash-flow summaries
- Automated reports
- KPI summaries
- Meeting preparation
- Exception alerts
- Task follow-up
- Information consolidation
- Board pack drafting
- Decision logs
Have another process in mind? Tell us what you want to automate.
Common questions
The practical version.
A chatbot answers questions in a conversation. An agent pursues a goal: it plans steps, uses tools such as search, documents, spreadsheets and your CRM, checks its own work and delivers a result. A chatbot is one of the interfaces an agent can sit behind.
Only what you connect: specific folders, systems or tables, read through scoped credentials. Nothing is used to train public models, and the agent cites the source for every answer it gives from your documents.
Three layers. Permissions limit what it can touch. Confidence thresholds pause it when unsure. Approval gates put a person in front of any external or irreversible action. Every step is logged so mistakes are visible and correctable.
The model is chosen per job: OpenAI, Anthropic or an open model hosted privately where data must not leave your environment. Orchestration runs on n8n, Make or custom code, depending on what your team can maintain.
A scoped single-job agent typically goes from diagnostic to supervised live use in two to four weeks. Multi-agent workflows are built in increments, each one proven before the next is added.
Proof
AI Agents in practice
Real engagements, dated results, client names withheld.
B2B consultancy automation stack
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Read the case studyUS live chat SaaS
Continuous consulting engagement. Live chat & AI chatbot SaaS.
Read the case studyMulti-location repair network
One measurement layer across a network of locations.
Read the case studyFree 15-minute diagnostic
Tell us the job you would hand to a capable new hire.
We will tell you whether an agent can run it, what it would need access to and where a person should stay in the loop.