Let an agent handle the lookups, checks, and handoffs that keep pulling your team away from their work. Give an AI agent a defined job, the right system access, and clear limits. Keep people in control of the actions that matter.
Give the repetitive work a defined owner and a clear path forward. Start with one job the agent can perform, your team can check, and your business can measure.
Every request means opening three systems, checking the details, and deciding who needs to do what next.
A useful response still leaves someone to find the record, draft the update, and move the task forward.
It works on the easy examples. Nobody has settled the permissions, exceptions, approvals, or ongoing costs.
Arlo is our own agent product for marketing agencies. It connects questions in plain English to live analytics data, bringing the information together without another round of manual report building.
Choose the workflow before the technology. These are starting points to assess against your systems and the consequences of getting it wrong.
Your team keeps opening the same systems to gather context and decide what happens next.
A request is read, the relevant records are checked, and a proposed action reaches the right person for approval.
Handling time, successful handoffs, and requests that still need manual intervention.
Routine questions fill the inbox, but answering them requires more than a generic FAQ.
The agent checks the customer’s record and approved knowledge, drafts an answer, and escalates anything outside its remit.
Time to a reviewed response, answer accuracy, and escalation quality.
The numbers exist, but someone loses hours finding them and explaining what changed.
The agent reads approved sources, assembles a report with references, and flags missing or conflicting information for review.
Preparation time, source accuracy, and corrections needed before a report is shared.
The visible answer is one part of the system. These are the controls and connections we scope around it.
Choose one workflow, name its owner, and agree the outcome, boundaries, and baseline.
Set up the tools and data the task needs, with explicit read and action permissions.
Review working software each week. Start with the agent proposing actions your team can check.
Use real examples, missing information, tool failures, and exceptions. Agree the release checks.
Train the users, stage the launch, and make approvals, escalation, and pausing straightforward.
Review task success, manual intervention, and running costs. Increase scope when the evidence supports it.
A senior engineering team on a monthly basis, sized to how fast you want to move. Any tier can work through the same roadmap. A smaller team takes more months; a larger team moves more of it forward at once.
An agreed first release, then weekly progress
Work through the roadmap in priority order at a steady monthly budget.
An agreed first release, then weekly progress
Add capacity to move the build, integrations, and rollout forward together.
An agreed first release, then weekly progress
Bring more capacity to a tighter timeline or several connected workstreams.
Three-month minimum, thirty days' notice. You own the code from the first commit. Model usage and third-party tool fees are separate. We estimate these against the workflow and agree budgets before rollout.
You own the repository, prompts, and workflow-specific tests we build for you. We document how the system works so another team can maintain it.
Agree who owns each model, cloud, and integration account. Keep usage, credentials, and access responsibilities visible to your business.
Hosting, monitoring, backups, and security updates are included while we work together. Agree who reviews exceptions and how support continues after handover.
Define what the system can read and change, what it logs, and how long records are kept. Review provider terms and specialist requirements before using sensitive information.
Runs discovery and architecture on every project and writes code on all of them. Built Agency ERP, Arlo, and Ollie for our own operations first.
Owns the data model, migrations, and integrations. The person who makes QuickBooks, Stripe, and your spreadsheets agree with each other.
Builds the product surface: the screens your team uses every day, the permissions behind them, and the tests that keep them working.
Clear answers on timelines, pricing, ownership, and what shipping actually looks like with a senior engineering team.
Engineering retainers start at $7k per month, with $13k and $18k+ options for more delivery capacity. The tiers set the pace, not which workflows you are allowed to build. We agree the roadmap, dependencies, and likely duration together. There is a three-month minimum and thirty days’ notice. Model usage and third-party tool fees are separate.
We target a usable first workflow in 3–4 weeks once scope, access, and data are ready. That is a first release for review, not a promise that the whole operation is ready to run unattended. Integrations, testing, and rollout requirements determine the wider timeline.
If the steps are predictable, ordinary automation may be enough. An agent is worth exploring when a task requires interpreting information and choosing among permitted actions. We compare those options before recommending a custom build.
We plan for mistakes. Consequential actions can require approval, outputs are checked, and uncertain or failed cases go to a person with context. Evaluations reduce risk but do not make the system infallible. The workflow must have a workable recovery path.
Many assistants already support tools and business data. A custom agent is for a workflow that needs specific integrations, permissions, evaluations, and operational ownership that your existing setup does not provide.
Often, through an API or another supported integration. We check the available access, data quality, rate limits, and permissions before agreeing the scope. If a system cannot support the required actions reliably, that changes the design.
It depends on task volume, model choices, context size, and the tools involved. We estimate usage before launch, set budgets and alerts, and track cost per completed task alongside quality. These fees are separate from the engineering retainer.
Yes. Arlo is our own product for answering marketing analytics questions using connected live data. We also use AI workflows inside our Agency ERP. We distinguish those examples from external client work.
That depends on the architecture and providers we agree to use. We map which data reaches each tool or model, review provider retention and training terms, and set access and logging rules before using sensitive records.
AI implementation is the better starting point when the priority is still unclear. We map the work, compare opportunities and readiness, and choose a useful first release before deciding whether it needs an agent.
Tell us what you're building or fixing. We'll come prepared with questions, not a pitch, and you'll leave the call with a straight answer on fit, which tier, and how soon you'd have version one in your hands.
Or email hello@buildfutur.com