AI Agents & Automation.
An AI automation agency builds software that carries out defined operational work inside the systems you already run, rather than selling you another tool to log into. Molo is a London AI agency that designs, engineers and maintains AI agents and workflow automation for sales, marketing, finance and operations teams: lead qualification and routing, document and invoice processing, support triage, reporting, quote preparation and CRM hygiene. We build against your real processes and your real data, with a human reviewing anything that carries commercial or regulatory risk, and we cost the maintenance honestly before you commit.
- Discipline
- AI Agents & Automation
- Studio
- London
- Engagements
- Project · Retainer
- Brands served
- 200+
How we work
Bespoke AI agents that take repetitive work off your team's plate.
A chatbot answers questions. A Zapier or Make scenario follows a fixed path: when this happens, do that, in that order, every time. An AI agent sits between the two and is different from both. You give it a goal, a defined set of tools it is permitted to use (read a CRM record, query a database, draft a reply, call an internal API), and it decides which to use and in what order for the case in front of it. That judgement is the point: it absorbs the awkward minority of inputs that break rule-based workflows, the enquiry phrased five different ways, the invoice with the reference in the wrong field, the ticket that is really two tickets. It is also the risk, which is why permissions, scope and review design matter far more than which model sits underneath.
Automation pays where work is high in volume, low in judgement and expensive in senior time: qualifying and routing leads, triaging first-line support, pulling numbers out of documents and into a system of record, drafting the same category of reply for the hundredth time, keeping a CRM tidy, reconciling two systems that disagree. It does not pay in four situations, and we will tell you when you are in one of them. When volume is too low, because an hour a week of manual work will not repay a build and its upkeep. When the process is genuinely undocumented and every case is decided on feel, because you cannot automate a decision nobody can articulate. When a mistake is unrecoverable or unbounded: money moved, contracts committed, records deleted. And when the process is badly designed rather than slow, in which case automation simply produces poor outcomes faster and at greater volume.
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Most of the engineering in an agent build has nothing to do with AI. It is integration, state and edge cases: authenticating against your CRM, mapping fields between systems that model the same customer differently, deciding what happens when an API times out halfway through a multi-step action, making retries safe so a duplicate run does not create a duplicate record, and keeping an audit trail of every action and its reasoning. That is ordinary software engineering, and it is where builds fail. Our evidence for it is deliberately non-AI: a 3D configurator for Glass Openings handling more than 120 products, over 70 materials and more than 30 glazing variations with live product logic; and a custom subscription engine for Circle Of Life where order generation, billing and delivery scheduling run on a workflow layer, so adding a plan tier is a configuration change rather than a rewrite.
We design every agent on the assumption that it will be wrong sometimes, because it will. That means a defined confidence threshold below which the agent stops and asks; escalation to a named person rather than a shared inbox nobody reads; reversibility on anything touching a customer or a ledger; rate limits so one failure cannot cascade across thousands of records before someone notices; and logging detailed enough to reconstruct what happened. High-stakes steps keep a human approving on the critical path by design: the agent prepares the work, a person commits it. That is a slower system than the demo you were shown and a considerably cheaper one than the alternative.
You cannot tell whether an agent worked without a baseline, so we take one before building: how long the task takes today, how often it runs, who does it, what it costs fully loaded, and how often the current human process gets it wrong. That last figure is usually higher than teams expect, and it is the fair comparison. After deployment we track containment, the share of cases completed with no human involvement, plus escalation rate, error rate against a held-out set of real historical cases, cycle time, and cost per case including model usage. If containment stays low or escalations stay high, the scope was wrong, and we narrow it rather than defend it.
Agents are not a project that finishes. Models are deprecated and their replacements behave differently on your edge cases. The APIs you integrated against change. Your own process drifts as products, pricing and teams change. Prompts and tool definitions need regression testing the same way code does. Budget for ongoing ownership from the start, whether that is us on a retained basis or your team after a documented handover, and treat any proposal that is silent on maintenance as incomplete rather than cheap.
One thing we will not do is show you an AI case study we do not have. Molo has not published a client AI agent deployment, and a good deal of what passes for proof in this market is a screenshot and a number nobody can verify. What we can evidence is the engineering that agent work actually depends on: complex product logic and systems integration, workflow architecture built to absorb change, and product design on Lumina, an AI reporting platform we shaped around automating agency reporting workflows. Alongside that sits a measurement culture from growth engagements where results were visible in the client's own analytics rather than ours. Judge us on method, on the questions we ask in the first meeting, and on whether we are willing to talk you out of something. We work with businesses across London and the UK, most often where an operations, sales or finance lead can already name the process that is costing them and roughly what it costs, which is usually enough to establish in a first conversation whether a build is worth scoping at all.
Capabilities
Everything you need under one roof.
Each engagement is shaped around the outcome you’re hiring us for. Below is the full toolkit our team brings to ai agents & automation engagements.
- Lead qualification, enrichment and routing
- Sales follow-up and CRM hygiene automation
- Customer support triage and reply drafting
- Document, invoice and email data extraction
- Quote and proposal preparation
- Reporting and dashboard automation
- Internal knowledge and search agents
- CRM, ERP, helpdesk and bespoke API integration
- Human-in-the-loop review interfaces
- Evaluation suites built on real historical cases
- Monitoring, alerting and audit logging
- Documentation, handover and team training
Our Process
A clear path from brief to launch.
We’ve refined this process across hundreds of engagements. It scales from a four-week sprint to a multi-year partnership without losing momentum.
Audit
We map how the work is actually done rather than how the process document says it is done, who touches each case, where it queues, which decisions require judgement and which are rules in disguise. We come out with a shortlist of candidates scored on volume, variability, integration difficulty and the cost of getting it wrong.
Baseline
Before anything is built we cost the process as it stands: time per case, frequency, fully loaded staff cost, rework, delay cost, and the current human error rate. Without this number there is no honest way to judge the result later, and no fair comparison for the agent to be measured against.
Design
We define what the agent may and may not do: the tools it can call, the data it can read, the actions it can take unsupervised, the confidence threshold at which it stops, who it escalates to, and what the human review interface looks like. Scope is drawn deliberately tight for the first release.
Build and integrate
Senior engineers build the agent and, more significantly, the integrations around it: authentication, field mapping, safe retries, idempotency, timeouts, rate limits and audit logging. The model layer is a component of a system, not the system itself, and it is treated accordingly.
Pilot and evaluate
The agent runs alongside the existing process rather than replacing it, scored against a held-out set of real historical cases and reviewed by the people who do the work today. We measure containment, escalation rate and errors, and we narrow scope where the evidence says the boundary was drawn wrongly.
Operate and maintain
Production monitoring, alerting on drift and failure, regression testing when models or APIs change, and periodic review as your process evolves. Ownership is agreed up front: retained by us, or handed over with the evaluation suite and documentation so your team can carry it.
Outcomes
Proof, in the only currency that matters.
Reduction in manual ops time across deployed workflows
Faster lead response time after rollout
Typical first-agent rollout timeline
FAQ
Common questions.
Don’t see your question? Email info@molo.agency and we’ll come back within one working day.
What does an AI automation agency actually do?
It builds software that performs defined operational work inside your existing systems, rather than selling you a subscription. In practice that means identifying which processes are worth automating, engineering the integrations into your CRM, database and internal tools, designing where a human reviews the output, and maintaining the whole thing as your systems and the underlying models change. The AI component is usually the smallest part of the build.
How is an AI agent different from Zapier or Make?
Zapier and Make follow a fixed path: a trigger fires and predetermined steps run in order. An AI agent is given a goal and a set of tools it is allowed to use, and decides which to use for each case. That handles inputs which vary, the same request phrased five ways, where a rules-based flow breaks. If your process genuinely is fixed, Zapier is cheaper and more predictable, and we will tell you so.
How much does AI automation cost?
We will not quote a figure without seeing the process, because the cost drivers are integration complexity rather than the AI itself. The variables are how many systems the agent must touch, whether those systems have usable APIs, how much variation exists in the inputs, how strict the review requirements are, and the running cost per case once live. Ask any agency to break those out. One well-scoped workflow is a different order of expense to an operations-wide programme.
What happens when the AI gets it wrong?
It should stop rather than proceed. We build a confidence threshold below which the agent escalates to a named person, reversibility on any action touching a customer or a ledger, rate limits so a single failure cannot cascade across thousands of records, and an audit log detailed enough to reconstruct what happened and why. High-stakes steps keep a human approving on the critical path, so a wrong answer is caught before it becomes a wrong action.
Which processes should we not automate?
Four categories. Low-volume work, where the build and its maintenance cost more than the hours saved. Undocumented processes where each case is decided on experience nobody can articulate. Anything where a mistake is unrecoverable or unbounded, such as moving money, deleting records or committing contractually. And processes that are badly designed rather than slow, where automation just produces poor outcomes faster. We would also think hard before removing human contact that customers value.
How long does it take to build an AI agent?
It depends far more on your systems than on ours. A single workflow against tools with clean APIs and a clear internal owner moves quickly. The same workflow against a legacy system with no API, unclear data ownership, or three stakeholders who disagree about the process takes considerably longer. We deliberately scope the first agent narrowly so it reaches production and produces evidence, rather than spending months on a programme that has never met reality.
Will an AI agent work with our CRM and internal systems?
Usually yes, and that integration is where most of the effort goes. We work with HubSpot, Salesforce, Pipedrive, common ERP and helpdesk platforms, databases and bespoke internal APIs. The practical question is not whether a connection is possible but what it costs: systems without APIs need alternative approaches, and field-level mapping between platforms that model the same customer differently is real work that gets routinely underestimated.
Do we have to give the AI access to our customer data?
Only what a task requires, and only in the way you sanction. We scope permissions to the minimum each agent needs, keep credentials in your infrastructure where practical, log what is accessed, and design around your data-protection obligations from the start rather than retrofitting them. If a workflow can be built without exposing personal data, working on identifiers or redacted records, that is the version we propose.
How do you measure whether the automation worked?
Against a baseline taken before we build: how long the task takes today, how often it runs, its fully loaded cost, and how often the current human process gets it wrong. Afterwards we track containment, meaning the share of cases completed with no human involvement, plus escalation rate, error rate against held-out real cases, cycle time, and cost per case including model usage. Low containment means the scope was wrong, and we narrow it.
Who maintains the automation after launch?
Someone has to, and it should be decided before the build starts. Models get deprecated and their replacements behave differently on your edge cases, the APIs you integrated against change, and your own process drifts as products and pricing move. We offer retained ownership with monitoring and regression testing, or a documented handover including the evaluation suite so your team can maintain it. Treat any proposal silent on maintenance as incomplete.
AI search visibility and GEO
As AI assistants become part of how customers research and choose suppliers, being referenced in their answers matters. Generative Engine Optimisation strengthens the signals AI systems use to understand and trust your brand.
Real businesses. Real results.
Every project we ship is measured against commercial outcomes, not vanity metrics. These are three of the brands we’ve helped scale this year.
Let’s discuss a project.
Tell us about your business, your ambition, and your timing. We’ll come back within one working day with the right shape of partnership for what you’re trying to build.
- info@molo.agency
- Phone
- +44 207 255 2221
- Studio
- 1st Floor Woodgate Studios, 2-8 Games Road
Cockfosters EN4 9HN
United Kingdom