AI-Powered Apps & Integrations
Custom applications with AI inside. We design them, build them, and keep running them.
OUR APPROACH
We Build the Application
The AI Is Inside It
We build software. The software we build has AI as a core feature.
- Entirely new systems: you come to us with a product idea and no codebase, and we build it
- Data intelligence layer: we connect your accounting, CRM, and e-commerce systems into a single new application with a conversational AI – machine layer on top
- SaaS replacements: an internal tool, a customer-facing web app for you to resell, or
- AI added deeply to software you already run
The application is the deliverable. Example: Voice Intelligence Platform started as a product idea. We built the POC, validated the approach, then shipped it.
CAPABILITIES
What We Build
Some examples of what we build include:
🚀
Greenfield AI Applications
Net-new systems built from a product idea. You have a concept, no codebase, no existing app. We build the POC, validate the approach, and ship the production application.
🔗
Data Intelligence Layers
New software that connects your existing tools into a single application. Accounting, CRM, and e-commerce pulled into one ML and data platform with a conversational AI layer on top.
📦
SaaS Replacements
Save hundreds of thousands of dollars by owning the software instead of subscribing forever. We build the equivalent of the SaaS tool your team relies on, customized to how you actually work, with AI features built in from the start.
🛠️
Internal Tools and Intranet Apps
Operations dashboards, research tools, ops apps, intranet portals. Software your team uses every day, with AI as a core feature: classification, summarization, decisioning, retrieval.
📱
Customer-Facing Web Apps
Products your customers use, with AI features built in from the first release: recommendations, in-app chat, decisioning, semantic search, generation. The application is the product.
🧩
Enhancements to Existing Software
You already have an app and want AI added deeply. We work inside the platforms you run (HubSpot, Salesforce, your own SaaS) using their documented extension points. One shape among many.
HOW WE BUILD
Engineering Discipline That Holds Under Traffic
We focus on the entire application, with specific discipline around the AI. And we keep running it: ongoing support is part of every engagement.
Cost-Optimized Model Routing
Route requests to the cheapest model that meets the quality bar, fall back to larger models only when needed, and cache aggressively. Cost graphs that flatten instead of climb with adoption.
Security and Access Control
Every integration ships with explicit access scopes, audit logging, PII handling rules, and secrets management aligned with your existing security posture. Outside review is available via our AI risk and security assessment.
Low Error-Rate Validation Gates
Model output is checked against deterministic rules before it reaches the user or the database. Schema validation, plausibility checks, fallback paths when confidence is low. What turns a chatbot demo into something your team actually trusts.
Multi-Agent Hand-Off Contracts
When a workflow uses more than one model or step, the hand-off is the most common failure point. We design explicit contracts: what gets passed, what’s validated, what triggers a retry, what triggers human review.
UX for Humans and Agents
AI features need a different interaction design. Where does the model take initiative? Where does the human stay in control? How is uncertainty surfaced? We design these decisions deliberately instead of letting the chatbot default eat the product.
Ongoing Support, Baked In
The build doesn’t end at launch. Every engagement includes ongoing support: cost monitoring as traffic grows, security maintenance, reliability tuning, and improvements as the data evolves. Our care and ongoing improvements don’t stop when it first goes live.
THE DISTINCTION
A Worker, or an Application?
Same underlying technology, different shape.
One does a job autonomously; the other is a product.
You Want an Autonomous Agent.
A system that does a specific job on its own, on a schedule, with no human in the loop for the work itself. The agent is the worker. No UI for end users; the output is the deliverable.
✓ Recurring task that runs on a schedule
✓ No end-user UI needed
✓ Scheduled or event-triggered work
✓ You want software to own the task end-to-end
That’s our managed autonomous AI agents service. Continuous operation, weekly check-ins, you don’t operate the system.
You Want an AI Application.
A software product your team or your customers use day-to-day, with AI as a core feature. The application is the deliverable; people use it. AI is one of several capabilities the product offers.
✓ End-user UI required
✓ You want a product to own
✓ You’re building or replacing software
✓ AI is one of several core features
That’s this page. Project-based engagement, code you own, ongoing support included.
WHEN TO TALK TO US
Where We Usually Come In
The AI in Production Isn’t Reliable
Hallucinations leak into customer responses. Outputs vary too much between identical inputs. Support tickets cite “the AI said.” We add validation gates and routing logic so the system behaves consistently.
You Need Production Expertise
The internal pilot worked. The production version doesn’t. You need people who have shipped AI features past staging and kept them running under real traffic. That’s most of what we do.
Costs Are Out of Control
The AI bill is growing faster than adoption justifies. We rebuild the routing layer so cheaper models handle the easy cases and the expensive models only get the requests that actually need them.
A Security Audit Is Coming
SOC 2, ISO, internal review, or a customer who just asked the question. We tighten access scopes, audit logging, and PII handling on existing AI integrations, or run a full AI risk and security assessment.
You’re Building AI Trust Day One
First AI feature shipping to customers. The decisions you make about uncertainty, escalation, and explainability now will shape how users feel about the product for years. We’ve made these decisions a lot.
You’re Architecting a Multi-Agent System
One big prompt isn’t holding up. You’re moving to specialized agents with hand-off contracts. We’ve shipped this pattern in production and can help you avoid the orchestration mistakes that look fine in testing.
THE STACK
Any Stack, Done Right
Below is some of the toolkit we build on. No vendor lock-in, no proprietary platforms, no rewrites if your stack changes. We are proudly tech-agnostic.
Apps
React, Node, Python, Django, Next.js, TypeScript
AI Layer
LangChain, vector DBs, OpenAI, Anthropic, Google
CRM
HubSpot, Salesforce, ActiveCampaign, GHL others
Infra
AWS, Azure, GCP, Supabase, Docker
CASE STUDY
Voice Intelligence Platform: Built From Scratch
A client came to us with a product idea: no existing app, no codebase, just a problem and a thesis about how AI could solve it. We built the POC to validate the approach, then the production application. Twilio, Microsoft Teams, Supabase, n8n, and AWS pulled together into a new application with AI at its core. The deliverable wasn’t intelligence added to an existing tool; it was the application itself.
DEEP DIVES
Deep Dives: Related Reading
PRODUCTION DISCIPLINE
Production posts you should read first
AI Cost Optimization: A Practitioner Framework
How model routing, caching, and fallback paths flatten the cost curve as adoption grows, instead of letting it climb linearly with traffic.
Cost discipline
Agent Governance in Practice: Securing Production AI Agents
Access scopes, audit logging, and PII handling rules for AI systems that touch your data and your users’ data in production.
Security & governance
Anthropic’s Multi-Agent Blueprint: What Production Adds
What it takes to move multi-agent designs out of demos and into systems that handle traffic, hand-offs, and failure modes without manual babysitting.
Multi-agent
BUILDING DECISIONS
How we think about what to build
Build, Don’t Buy AI Agents: A Practitioner’s Guide
When the buy-a-SaaS-tool default falls apart, and what owning the build actually gets you: control over data, costs, and what the system can do next.
Build vs buy
Hydraulic 3D Simulation Built With Zero Human Code
A case study of a 3D simulation product shipped entirely through AI coding agents. What the build process looked like, and what it took to make it production-grade.
Case study
AI Agent Case Study: Voice Intelligence Platform
How an AI coding agent built a production voice intelligence platform from concept to ship. What worked, what required human judgment, and what changed about the build process.
Case study
PRICING
Project-Based, Scoped Up Front
$6,000–$50,000+
per project
Smaller internal tools and targeted enhancements sit toward the lower end. Greenfield applications, data intelligence platforms, and customer-facing products with custom architecture sit toward the higher end. We scope and quote before any work begins.
QUESTIONS
Frequently Asked Questions
WHAT CLIENTS SAY
Teams We’ve Worked Alongside
Fountain City has been very good at providing the desired results without a lot of hand-holding.
They always have our best interests at heart and come to the table with excellent suggestions based on their experience.
Fountain City offers an excellent personal touch and is very approachable while being very professional still.
Fountain City’s results, honesty and attention to detail stand out. They’re very realistic as well.
I’m always confident they’ll take the lead to ensure that the project runs smoothly and in the manner that I expected.
Results and relationships matter to this team, and it makes for a pleasant and productive experience.
Let’s Build the Application
Tell us what the application needs to do. We’ll scope it, quote it, and tell you honestly whether an application with AI inside or an autonomous agent is the right fit for the job.
