C-03Applied AI & Automation
AI automation services for revenue and operations teams
The short answerquotable
Applied AI and automation at AiS LABZ is a consultancy service that moves repetitive, judgement-light work inside a business to software. A two-week diagnostic ranks candidate workflows by hours saved, error rate and revenue touched. We then build the top one: an LLM assistant grounded in your own documents, a scoring model that ranks leads or tickets, or a workflow automation across the systems you already run. Every build ships with a human approval gate and an audit log, and most pay back within one to three months.
What is an AI automation agency and what does it build?
Bounded systems that act when confident, escalate when not
An AI automation agency moves repetitive, judgement-light work to software that decides within limits you set. Expect three builds: LLM assistants on your own documents, scoring models that rank leads or tickets, and workflow automation across the tools you already run. Demand a human queue for everything else.
- LLM assistants and copilots grounded in your own data
- Lead and ticket scoring models that rank the call list
- Workflow automation across CRM, helpdesk, billing and email
- Human approval gates and audit logs on every automated decision
Why do most AI pilots stall before they reach production?
Pilots die at the integration, not the demo
A sandbox demo is easy. Reading the live CRM, respecting permissions and updating records without breaking downstream reporting is the hard part, and most pilots skip it. Scope the integration first. Then pick the dull, high-volume workflow over the impressive one, because its payback funds the rest.
- Scope the integration before the model
- Name an owner for the integration work on day one
- Board-report drafting saves four hours a quarter
- Ticket routing saves four hours a day. Start there.
The LABZ protocol· for applied ai & automation
Diagnose. Model. Build. Compound.
The same four phases run every engagement, in Waltham, London or Riyadh. Here is what each one means for this discipline.
Diagnose
Audit team workflows for two weeks; rank by hours saved, error rate and revenue touched; estimate payback.
Model
Define data sources, permission scope, approval rules and success metrics for the top workflow before writing code.
Build
Ship two-week increments with a named owner: retrieval or scoring model, integrations, human queue, audit log, on live data.
Compound
Tune confidence thresholds from real approvals, widen the automation, then start the next workflow on the list.
How does an LLM assistant on company data actually work?
Retrieval, not training. Every answer cites its source.
The assistant searches your documents, tickets and records for the passages relevant to the question, then hands only those to the model with instructions to answer from them and cite. Your data is never trained on. Scope access to existing roles, redact sensitive fields, log every request.
- Retrieval over your documents, not a model trained on them
- Answers cite the source passage so staff can verify
- Permission-scoped access mirrors your existing roles
- Redaction of sensitive fields before the model sees them
What does a lead scoring model do for a sales team?
Same reps, pointed at the leads most likely to pay
A lead scoring model learns from your historical wins and losses, predicts close probability and value for every inbound lead, and reorders the queue so sales calls the highest-value lead first, not the newest. The same method ranks tickets by churn risk and accounts by expansion potential.
- Trained on your own CRM history, not a generic dataset
- Accuracy reported on a held-out set before go-live
- Retrained on a schedule as your market shifts
- Every score shows its top reasons, so reps trust the rank
How do you keep AI automation safe and governable?
Every action carries a confidence. Low confidence goes to a human.
Confident, low-risk actions such as tagging a ticket execute automatically. Anything consequential, like a price change, waits in a human queue with the proposed action and reasoning attached. Record every approval, edit and rejection; that evidence tunes thresholds and becomes the audit trail compliance asks for.
- Confident, low-risk actions execute automatically
- Everything else routes to a human queue with reasoning attached
- Approvals and rejections feed evaluation sets that tune thresholds
- Full audit log of decisions, inputs and outcomes
What you receive
Deliverables, not decks
Every item below is a contractual deliverable with a named owner and a date. Documentation is part of the work, not an extra.
Workflow diagnostic report
Ranked automation candidates with hours saved, error rate, revenue touched and payback.
LLM assistant with retrieval
Grounded assistant answering from your records, with citations and permission-scoped access.
Lead or ticket scoring model
Trained on your history, ranks the queue with reasons, accuracy reported before go-live.
Workflow automation
Integrations across CRM, helpdesk, billing and email; records flow without re-entry.
Human approval queue
Review interface for lower-confidence actions: proposed action, reasoning, one-click approve or edit.
Audit log and evaluation set
Every decision recorded with inputs and outcome, feeding evaluations that tune thresholds.
What is the AI automation process from first call to live system?
Diagnose, Model, Build, Compound. Two-week increments throughout.
The diagnostic runs one to two weeks: map workflows, count hours, measure error rate, note revenue touched, rank by payback. If nothing clears the bar, the engagement ends there. Modelling fixes data, permissions, approval rules and metrics. Build ships in two-week increments with a named owner and live dashboard.
- Diagnose: one to two weeks, ranked list with estimated payback
- Model: data, permission model, approval rules, success metrics
- Build: narrowest version that runs on real data first
- Compound: widen the automation or start the next workflow
How much do AI automation services cost and how quickly do they pay back?
Fixed-fee diagnostic. Priced increments. Payback in one to three months.
Know the number before work starts. The diagnostic is a fixed fee; the build is priced per two-week increment after modelling; operation, retraining and monitoring run month-to-month after a three-month setup. Measure payback against the diagnostic baseline. If the first increment is off target, change scope or stop.
- Fixed-fee diagnostic, priced build increments, monthly subscription after setup
- Payback measured against hours, error rate and revenue baseline
- Stop at the end of any increment without penalty
- Senior-only pods: no paying to train juniors on your systems
Further reading from the AiS LABZ blog
Is this for you
A good fit and a bad one
Built for
- Teams with a repetitive, high-volume workflow explainable in an afternoon
- Businesses with CRM, helpdesk or document history to ground a model
- Leaders who want governance built in, not bolted on
Not the right service when
- Companies wanting a general chatbot with no defined workflow
- Teams unwilling to keep a human on consequential decisions
Common questions
Questions we get asked
An AI automation consultancy finds the repetitive, judgement-light work inside your business and moves it to software. At AiS LABZ that means three things: LLM assistants grounded in your own documents, scoring models that rank leads or tickets by likely value, and workflow automation that connects the systems you already run. Every automation ships with a human approval gate and an audit log, so you can see what it decided and why.
Yes. We build retrieval over your own documents and records so the model answers from your material rather than from the open web, with access scoped to the same permissions your staff already have. Sensitive fields can be redacted before they ever reach a model, and every request and response is logged. You keep the data where it lives — we govern how the model is allowed to reach it.
Most of our automations pay back inside one to three months, because we start with the highest-volume, lowest-risk workflow rather than the most impressive one. The two-week diagnostic ranks candidate workflows by hours saved, error rate and revenue touched, and we build the top one first. If nothing in the list clears the payback bar, we tell you that instead of building it.
The diagnostic is a fixed fee. The build is scoped after modelling and priced per two-week increment, so you see the full number before work starts and can stop at the end of any increment. Once live, monitoring, retraining and support run month-to-month after a three-month setup period, cancellable at any month end.
As a partner, not a replacement. Our senior-only pod builds the model, retrieval and approval logic; your engineers own the integration points, because they know your systems best. Work runs in shared two-week increments with a live dashboard. At handover you get documented code, evaluation sets and runbooks, so your team can run and extend the system without us.
Three things. An hour with the people who do the workflow today, so we can map and time it. Read access to the CRM, helpdesk or document store the automation draws on, scoped to the project. And one decision owner to approve the ranked list after the diagnostic and sign off the approval rules. We provide everything else, including infrastructure.
Three risks: a model acting on wrong information, a model exposing data it should not see, and a team losing sight of what the system does. Confidence thresholds and a human queue handle the first. Permission-scoped retrieval and redaction handle the second. An audit log and live dashboard handle the third, so the system is never a black box.
Free 15-minute diagnostic
Find the constraint first.
Fifteen minutes with a senior on the Applied AI & Automation pod. We tell you what we would look at, what it would cost and whether it is worth doing — including when the answer is no.