AI that actually works in production — not just in demos.
AI Product Development for Startups That Need Results, Not Experiments
Everyone is adding AI to their product. Most of it is unreliable, expensive to run, and doesn't survive contact with real users. We build AI features and products that are designed for production — with the guardrails, cost controls, and UX quality that turns a demo into a feature people depend on.
Your AI feature works in demos but breaks in production
Inconsistent outputs, hallucinations in edge cases, and unpredictable latency are the difference between a prototype and a product. Getting AI to production quality is an engineering problem, not a prompting problem.
Your AI API costs are out of control
Token costs scale with usage in ways that aren't always obvious upfront. Without prompt optimization, caching strategies, and model selection discipline, your margins disappear before you hit profitability.
Users don't trust the output
AI output that can't be verified erodes trust fast. If users can't tell when the AI is confident vs. guessing, they stop using the feature. UI and reliability engineering solves this.
What We Offer
AI Chatbots & Assistants
Context-aware AI assistants with memory, document grounding, and guardrails — built for your specific use case, not generic chatbot templates. Integrated into your product UI with proper loading states and fallback handling.
LLM-Powered App Features
AI writing assistance, auto-summarization, classification, extraction, and generation features — built into your existing product with proper rate limiting, cost controls, and user feedback mechanisms.
Workflow Automation
AI agents and pipelines that take real actions — processing documents, updating records, sending communications, and routing tasks — with human-in-the-loop controls where the stakes require it.
Document & Data Processing
RAG (Retrieval-Augmented Generation) systems that let users query their own documents, structured data extraction from unstructured inputs, and AI-powered analysis pipelines for large document sets.
AI Integration Into Existing Products
If you have an existing product and want to add AI capabilities, we scope the integration cleanly — identifying where AI adds real value, where it creates risk, and how to build it without disrupting what's already working.
Why Choose Us
- We treat reliability as a first-class requirementAn AI feature that works 80% of the time isn't a feature — it's a liability. We engineer for the edge cases, build fallback logic, and measure accuracy before shipping.
- Cost optimization is part of the designWe model token costs at realistic usage volumes before the build starts. Prompt compression, caching, model tiering, and batching are part of the architecture — not afterthoughts.
- We build the full product, not just the AI layerThe AI is one component of a product. The UI that presents it, the data pipeline that feeds it, and the evaluation system that improves it are equally important. We build all of it.
Tech Stack
How We Work
A clear path from idea to launch.
Problem Mapping
Identify which specific workflows have the most to gain from AI. Define what 'good output' looks like. Set accuracy and reliability expectations before any model is selected or prompt is written.
Architecture & Model Selection
Choose the right model for the task (not always GPT-4), design the retrieval layer if needed, plan the data pipeline, and estimate production costs at realistic usage volumes.
Prototype & Evaluation
Build the core AI flow and evaluate it against a representative sample of real inputs. Measure accuracy, latency, and cost before committing to the full build.
Production Build
Full implementation with error handling, fallback logic, cost controls, output validation, and the product UI that makes the AI output useful and trustworthy to the end user.
Iteration & Improvement
AI products improve with usage data. We build logging and evaluation infrastructure so you can systematically identify where the model is underperforming and improve it over time.
What You Get
- An AI feature that works consistently in production, not just in controlled demos
- Cost-optimized prompt and model architecture that scales with your user growth
- User-facing AI with proper loading states, error handling, and trust signals
- Logging and evaluation infrastructure to improve output quality over time
- A clear understanding of where AI adds value in your product — and where it doesn't
Best For
- Startups building AI-native products from scratch
- Existing SaaS products adding AI features to improve core workflows
- Founders who have a GPT wrapper prototype and need to turn it into a real product
- Teams that built an AI demo that works in controlled conditions but breaks with real users
| What matters | GPT wrapper approach | Generic dev agency | Frontail |
|---|---|---|---|
| Production reliability | Poor | Inconsistent | Engineered for it |
| Cost optimization | Not considered | Rarely | Built into architecture |
| Evaluation framework | None | Manual QA only | Systematic logging + eval |
| AI UX quality | Basic | Low | Production-quality |
| Prompt engineering depth | Surface level | Low | Core skill |
| RAG / retrieval systems | Not offered | Rarely | Full implementation |
Questions
Which AI models do you work with?
Primarily OpenAI (GPT-4o, o1, o3), Anthropic (Claude 3.5, Claude 3 Opus), and open-source models via Hugging Face or Ollama for cost-sensitive use cases. We recommend the right model for each task based on the accuracy requirements, latency constraints, and cost envelope — not just the most popular option.
How do you handle AI hallucinations and reliability?
Hallucinations are an engineering problem as much as a model problem. We use structured output validation, retrieval grounding (RAG) for factual use cases, confidence thresholds, and fallback logic to catch and handle unreliable outputs before they reach users. We also build user-facing trust signals — citations, confidence indicators, and easy correction mechanisms — so users know when to verify.
How do you estimate the cost of running AI in production?
We model production costs in the Architecture phase, based on your estimated usage volume, average input/output token counts, and caching opportunities. We include this estimate in the proposal so there are no surprises. We also build cost monitoring into the production system so you can track spend in real time.
Can you build AI features without access to our user data?
Yes. We can build and test AI pipelines against synthetic or anonymized data and integrate them into your production environment with appropriate data handling controls. For products handling sensitive data, we can also scope on-premise or private deployment options.
Have an AI idea that needs to survive contact with real users?
Book a free AI scoping call. We'll map the workflow, estimate the cost, and tell you honestly whether the use case is ready for production.
Ready to Build
Your MVP?
Share your idea and we'll help you map the fastest path to launch.