Software shaped to your process, not the other way round
A production-grade SaaS application built for how your business actually runs — with AI applied to the steps that are slow today, shipped to your own repository, domain, and cloud account.
Generic tools make your team do the joining
Most businesses end up running on a stack of tools that almost fit. The gaps between them get filled by people — copying an order from an email into an ERP, reconciling two systems that disagree, re-typing the same customer detail for the third time. It works until volume grows, and then it quietly caps how much you can take on without hiring. The problem is rarely a missing feature; it's that no product on the market knows your process.
What a build actually includes
The product itself
A multi-tenant web application with authentication, roles and permissions, billing if you need it, and an admin surface your team can actually operate.
AI where it earns its place
Document extraction, classification, drafting, matching, search over your own data. Applied to the specific steps that are slow today — not sprinkled across the product as a feature list.
Integrations with what you run
Your CRM, ERP, inbox, payment processor, and internal databases connected properly through their APIs, with retries and error handling — not a brittle script on someone's laptop.
The operational layer
Logging, monitoring, alerting, backups, and an audit trail. The unglamorous parts that decide whether a system survives its first real month.
Ownership, stated plainly
Four things every engagement ships with. None of them are upsells.
Your GitHub repository
The repo is created in your organisation on day one. Every commit lands there as we work — you are never waiting on a handover to see the code.
Your domain, your cloud
Deployed to your AWS or cloud account under your own domain. No FluentOpsAI subdomain, no per-seat licence, no platform you have to keep renting.
Human-evaluated AI
Every AI step ships with an evaluation set and a confidence threshold. Below it, the case routes to a person instead of guessing. You see accuracy before it runs unattended.
Maintenance, if you want it
A monthly retainer for monitoring, fixes, and new work — or a clean handover to your own engineers with documentation. Your choice, and reversible.
Four phases, problem first
The order matters: nothing gets designed until we agree on which manual steps we're removing.
Understand the problem
We sit with the people doing the work and map the actual steps — what triggers them, how long each takes, where they break. Before anything is designed, we agree on which of those steps we are trying to remove.
Architect the solution
A solution architect defines the data model, the integration boundaries, and where AI is and isn't appropriate. You get a written architecture and a fixed scope for the first release.
Build in weekly increments
Production-grade code from the first commit, in your repository, reviewed weekly against real usage. No throwaway prototype, no six-month black box.
Prove it, then hand it over
Evaluation runs on real data, security review, deployment to your cloud, and documentation. Then either we maintain it or your team takes it — with the repo and the runbook.
Who this is for
- A team spending hours a day re-keying data between systems
- An off-the-shelf tool you've outgrown or are paying to work around
- A process where AI could help but accuracy actually matters
- You want to own the asset, not rent a seat forever
- A throwaway prototype or a pitch-deck demo
- A one-off script with no owner after launch
- A rebuild with no measurable manual work to remove
Start with the process, not the spec
Bring us the workflow that costs your team the most hours. We'll come back with an architecture and a first release you can scope.
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