AI & Automation
We research where AI actually adds value, build for your edge cases, and stay on after launch to keep it performing.
What We Do
We build AI systems that solve real business problems, not the generic AI that comes baked into every SaaS update. From automating document processing and customer support to deploying agents inside your existing tools, we research your workflows, choose the right approach (sometimes that approach isn’t AI at all), and validate every build under real production conditions before launch. Then we stay on to keep it performing as your business changes.
The RDV+ Framework
Every engagement at Stable Solutions follows the RDV+ framework. For AI and automation, that means eight steps grouped across four phases.
R Research & Solution Design
We start with the work itself, not the technology. Interviews with the operators who do the work today, a map of how time is being spent, and an audit of where your data lives and how clean it is. Most AI projects fail because the data was messier than the spec assumed. This step surfaces that early.
Not every problem needs AI. Some need workflow automation. Some need an agentic system. Some need a custom model. Some are best solved with off-the-shelf tools plus a thin extension layer. We recommend the approach that fits your problem and your ROI threshold, then walk through the reasoning with you before anything gets built.
D Development
We build the AI system against your actual data and your actual stack. Weekly demos, sandbox access, and a tight review cycle. The feedback loop is what makes sure the system reflects how your business actually works, not how we guessed in week one.
An AI system that lives outside the tools your team already uses doesn’t get used. We integrate the system into your CRM, your ticketing platform, your data warehouse, or wherever the work actually happens, so the value lands where your team already is.
V Validation
We run the system on production data and measure accuracy, response time, and cost per task against the targets set in Research. We test edge cases that would break a generic AI tool. We audit security and access controls, especially around customer and proprietary data. If the numbers hold, the hypothesis held. If they don’t, we tune until they do.
AI gets deployed every day with no plan for bias, no documentation of expected behavior, and no human in the loop on decisions that affect real people. We don’t ship that way. Every system we build goes through bias and accuracy testing, gets documented with a model card covering known limitations, includes human-review checkpoints on decisions that affect customers or employees, has data retention and training-data policies set with you, and includes audit trails so any output can be traced back to its inputs.
+ Ongoing Support
AI systems drift. Data shifts, your business evolves, and the system that performs at launch starts missing in month 8 because the inputs it was trained on aren’t the inputs it’s seeing. Monthly monitoring catches drift early, and retraining cycles keep accuracy where it needs to be.
Most agencies hand you a build and disappear. We don’t. New workflows, new integrations, and scope additions get handled inside the ongoing engagement, so the system keeps expanding into the places it can add value instead of staying frozen at launch.
Ongoing engagements include:
Our SLA commitment
Critical-issue response within one business day, where critical means production-down or actively losing data. If we miss that window, the next month of support is on us.
3-month minimum contract. No annual lock-in. Renew, scale, or end the engagement after each cycle. Adjust scope to match how the system is performing in production.
Capabilities
We orchestrate the work your team does today across the tools they already use. Programmatic where the workflow has scale or complexity. Low-code where speed and flexibility matter more. Either way, the system runs without anyone having to babysit it.
Agentic systems that handle multi-step tasks autonomously: gathering data, making decisions inside set guardrails, taking action across multiple tools, and reporting back. Built for the work that’s too dynamic for a static workflow.
Extract, classify, route, and analyze documents at scale. Contracts, invoices, applications, support tickets, internal records: the system reads them, understands them, and acts on what it finds.
Custom models trained on your data and tuned to your business questions. Churn prediction, demand forecasting, lead scoring, anomaly detection. Built when off-the-shelf analytics can’t model what makes your business specific.
Personalized communications and customer support at scale: nurture sequences, ticket responses, follow-ups, FAQ resolution. Designed to sound like your team, not like a chatbot.
The infrastructure that connects your AI system to your CRM, your data warehouse, your support tools, and everything else it needs to be useful. Built once, runs continuously, monitored for drift.
Potential Pain Points
Your team is buried in repetitive tasks: data entry, ticket routing, document processing. The only way to grow is hiring more people for the same work.
Churn signals, demand patterns, lead quality. The answers are sitting in your systems, but nobody has time to surface them, so decisions get made on gut feel.
You tried Zapier, ChatGPT, or a basic chatbot, and they hit a wall on your actual workflows. You need a system built around your business, not a generic tool you have to bend.
Schedule a call and we’ll walk through where AI fits in your stack, what it would take to build, and what kind of results you can expect.
