AI Services for
Small & Midsize Business
Three Services, One Experienced Team
AI should remove bottlenecks,
not create a new one called “AI project.”
At Fresh Ground, our approach keeps the work focused. We start by identifying the highest-leverage operational pain, scope the right workflow, build it, and put AI into use. The foundation of our services is over 25 years of software delivery experience…
AI: Where does AI fit?
You're running a lean team that's buried in the tedious stuff that fills everyone's timesheet: reports from QuickBooks are a week late, data is rekeyed between your CRM and customer service system, there’s no standard process for content, which takes a full day to produce. AI can fix all of that, but your team has neither the time nor the skills to build it.
Fresh Ground starts with a structured, no-pressure look at your workflows and systems. Under 10 minutes, no registration, no charge. We'll deliver a straight answer showing where AI aligns with your business now and where it doesn't yet.
Advise: Get Your First Win.
When Fresh Ground's services align with where you are, you don't get a strategy deck. You get a First Win, one real workflow, scoped small, built around a task your team already does by hand, and shipped in 30 days. Your First Win is way cooler than a pitch deck.
That said, the real goal is bigger than a single workflow. It's that moment your team asks what you can do next.
That's when Advise can become something bigger. Our first AI client brought us on for a narrowly scoped engagement…that didn't stay narrow. The value showed up in the work right away. Advise can grow into ongoing technical leadership: a fractional partner who's inside your systems, helping prioritize what's next.
Build: Go Deeper.
A First Win is proof that clears the way for doing more. Build is where we can tackle bigger projects that make people noticeably faster with deeper integration. We remove the friction that teams face every day with systems that make people noticeably faster.
We use the same production standards we have used for 25+ years, rigorously testing and shipping your new AI workflows.
It can also mean bringing new capabilities to your customers: chat interfaces, intelligent search, AI-assisted service. Production-grade, not a prototype. We never hand off a system and disappear. We remain the team that built it.
Fresh Ground AI Services at a Glance
Fresh Ground AI Services
We put AI to work inside your business and it keeps running after we leave. We are forward-deployed engineers, not just AI advisors. The proof standard is a useful workflow live in production in 30 days.
LLM Evaluation & Integration
We select the right model for your specific job, then wire it into the systems the business already uses.
We're model-agnostic by policy, Anthropic-first by default, and we choose with a measured comparison against your own data. Integration runs through MCP, so the model works inside real systems under established permissions instead of a copy of the data. We run this on ourselves too: one MCP server on Lambda serves the same retrieval to our people and our agents. Nothing hits production without evals and a written rollback plan first.
AI Agent Development
We create agents that do a named job activated on a schedule or by a trigger. We use scoped permissions, monitoring, and a person accountable for each agent.
Every agent must meet four requirements to ship: a written prompt, scoped permissions, evals, and a rollback plan. We install the agents and the operators, because keeping five agents running is a part-time job at minimum. Pretending otherwise is the most common way AI engagements fail six months later. Agents register into a dashboard carrying ownership, client mapping, schedule, and health. We set alerts for failure with a link to the full session trace. Fresh Ground runs eleven of our own this way now.
AI Workflow Automation
We take a manual process that costs the business time every day and make it run without manual effort.
We start from our AI assessment's ROI-ranked opportunity map. From there, we take the highest-value workflow first as a fixed-scope build with an acceptance test named before we start. Humans stay in the loop at every decision point.
We also tell clients which steps shouldn't be automated. That's what separates us from other vendors. For example,with one of our clients,: the owner was personally absorbing billing reconciliation and was trying to hire an administrator to escape it. Automation eradicated the friction.
AI Security & Governance
Governance is a gate, not a document. You must establish controls that make AI safe to run in your business.
Controls include what it can see, what it can do, what gets logged, and who signs off.
Write access is earned, not granted by default. Every action is written to an audit log. Outlier decisions stay with a human. Before we extend any agent's scope, we verify that if this current dysfunction were amplified at agent speed, would the outcome still be good? On the infrastructure side, this is work we already run on client accounts: IAM review, key rotation, GuardDuty, Config, patched instances, flow logs. MCP-layer logging is a shipped requirement on a live client build.
Document Processing
We turn unstructured text you already own (e.g., tickets, email, contracts, notes, transcripts) into something AI can search, route, or act on.
We treat this as a classification and retrieval problem with a measurable accuracy target. Work starts with a labeled sample from the customer's own data, and an accuracy threshold both sides agree to before anyone writes code. The sample is the benchmark: how a model scores on a public test has nothing to do with whether it can read your tickets.
Company OS
After we have successfully deployed a few AI workflows, we can build the operating layer you run on. It’s a machine-readable model of your business that agents read from and write back to.
Agent structure includes rules, processes, people, and decisions. Your company's knowledge stops living in people's heads and becomes something you can use.
We eat our own dog food here – our own OS is live. We built an MCP server to access the company’s wealth of experience There are eleven agents with prompts, permissions, evals, and rollback plans, fifteen composable capabilities, fourteen recorded architecture decisions, and a memory loop verified end to end. Watcher agents pull signal from our systems. Scheduled agents act on it. Every action is added to an audit log, and a retro agent tunes the prompts each week so good work compounds and problems don’t amplify.
Your Company OS engagement starts from your own world model. Write-back stays gated behind a foundation's audit until the basics are in place. Failures log capability gaps, and the gaps become the roadmap
We can add a deeply-experienced technology executive to your team. Our role is judgment. Your project manager drives, backed by our full service bench. A decision made in a strategy conversation can be built the same month without a third party. AI optional (for your environment!).
AI Strategy Consulting
We’ll help you decide where AI belongs in your business, in what order, and what each move is worth, before anyone builds anything.
We start from our AI assessment's ROI-ranked opportunity map. From there, we take the highest-value workflow first as a fixed-scope build with an acceptance test named before we start. Humans stay in the loop at every decision point. We also tell clients which steps shouldn't be automated. That's what separates us from other vendors. For example,with one of our clients,: the owner was personally absorbing billing reconciliation and was trying to hire an administrator to escape it. Automation eradicated the friction.
Fractional CTO Support
Add a deeply-experienced technology executive to your team…part-time.
Your FCTO covers strategy, architecture, vendor selection, roadmapping, and team leadership, with or without AI anywhere in the picture. No added headcount required.
Whether you need a CTO-level assessment with an initial roadmap, ongoing roadmapping, team leadership, and process work, or someone to oversee your technology function, we can support your team. Engagements start at five months at roughly a half-time commitment.
Something built, shipped, or solved, every month. Month 1 onboards, months 2 onward deliver results to streamline your business. The initial term runs six months, quarterly after that.
Internal Tool Development
You’re looking for a small application that no vendor sells. We offer a team to catalog needs and deliver a solution.
You may want a dashboard, an approval form, or something that replaces a spreadsheet and four emails. We start from our AI assessment's ROI-ranked opportunity map. From there, we take the highest-value workflow first as a fixed-scope build with an acceptance test named before we start. Humans stay in the loop at every decision point. We also tell You may want a dashboard, an approval form, or something that replaces a spreadsheet and four emails.
Build “small”, one useful thing shipped each month. We work from the process as it runs rather than from a requirements document. Then we ship narrow and early so you can review in week two instead of month four. We recently deployed a self-service IP allow-list application for a client whose staff were filing tickets to get network access. Another client got a procurement authorization platform. which steps shouldn't be automated. That's what separates us from other vendors. For example,with one of our clients,: the owner was personally absorbing billing reconciliation and was trying to hire an administrator to escape it. Automation eradicated the friction.
Custom AI App Prototyping
Fresh Ground can build a working version of an AI-powered application, fast enough to decide if it's worth building out completely.
We create a prototype that runs on real data (because synthetic data answers a question nobody asked). We build it on the stack we already operate at production scale. Your prototype earns its keep and doesn't need a rewrite to go live. There are three possible outcomes, named before we start: build it, change it, or stop. Stopping can be a successful outcome, and we say so before you pay for it.
Prototype to Production
An employee built a small application with an AI coding tool, and it works.
We can shore it up as software the company can reliably run: secure, scalable, tested, and maintainable by someone other than the solo developer.
We read the code, scanning what you have: dependencies, secrets sitting in the repository, authentication and authorization, what happens to customer data, tests (they probably don't exist), and the parts that could fail at higher workload. We share our findings list in fix order. You can stop right there, or we can work the list in the desired order. We almost always address security and data first, deployability second, and the exciting new features after that. We keep the original code wherever keeping it is reasonable, because rewriting working code to satisfy an engineer's taste is how a small job becomes a six-figure one.
The first deliverable answers a question you haven’t asked (yet): is this worth keeping at all? Sometimes it isn't, and we say so before wasting resources.
AI-Powered Digital Products
If you have a product you want to sell or operate, we can provide full-lifecycle delivery, with AI as part of what makes it worth buying.
We consider these build projects as “Large.” It's the work with our longest track record — over 600 projects across 22 industries and 22 countries, plus our own cloud-based SaaS product. Running a product of our own means we already know the things that quietly kill product engagements: metering, permissions, search that stays correct as the data grows, and a roadmap that survives contact with real customers. If you don't have a roadmap, we can lead.
AI-Data Modernization
Getting your data into a shape where the reporting is trustworthy is part art and part science.
We understand the goal: give AI something real to work with.This is about engineering, not strategy.
The sequence is to consolidate the sources, reconcile to a number the business already trusts (usually the general ledger), then put reporting on top of that. We're already running this on a multi-entity client now: sales history and margin per entity, reconciled to the GL, with live reporting on the other end.