AI Development
AI applications that work in production
We build custom AI agents, LLM integrations, and intelligent systems — engineered for reliability, security, and scale.
Most AI features die between the demo and production. Ours don't.
Anyone can wire a chat window to an LLM API and produce an impressive demo. The hard part is everything after: outputs that must be right every time, costs that must stay predictable at 10,000x demo volume, latency users will actually tolerate, and failure modes that don't corrupt your data or embarrass your brand.
We build AI systems with the assumption that the model will sometimes be wrong. That means structured outputs validated against schemas, confidence thresholds that route edge cases to humans, evaluation suites that catch regressions before deploys, and audit trails for every AI-initiated action.
This isn't theory for us. Our own products run governed AI in production — Jobsflix ATS ships a recruiter copilot with a human approval queue, and Nexus AI scores thousands of leads with explainable 0–100 reasoning. We make the same engineering decisions for your product that we live with in ours.
Capabilities
What we deliver
Proof
We've built this for ourselves
Don't take our word for it — inspect the products we run in production using exactly these practices.
Outcomes
Business impact you can measure
Operational Leverage
Automate the repetitive 80% of a workflow while routing judgment calls to your team.
Production-Grade AI
Ship AI that handles real workloads — not prototypes that break under pressure.
Competitive Moat
Build proprietary intelligence into your product that competitors can't replicate.
Our AI engineering principles
Every AI system we ship follows the same governance pattern we use in our own products: the model reasons, humans approve consequential actions, and deterministic systems execute. This keeps AI useful without making it dangerous to your operations or compliance posture.
We're deliberate about where AI belongs. Some workflows want a frontier model with deep reasoning; others want a fast, cheap classifier or no model at all. Picking the boring solution when it's better is part of the service.
- Structured outputs validated against schemas — no free-text parsing in critical paths
- Fallback chains across providers so a single API outage doesn't take down your feature
- Cost ceilings and token budgets enforced at the routing layer
- Human-in-the-loop approval for any action that touches money, messaging, or records
- Evaluation datasets built from your real cases, run on every prompt change
Process
How we work
Assess
Identify high-impact AI opportunities in your product and operations.
Architect
Design AI systems with the right models, data pipelines, and guardrails.
Build
Develop, test, and iterate with weekly demos and transparent progress.
Deploy
Launch to production with monitoring, fallbacks, and continuous optimization.
FAQ
AI Development questions, answered
Which AI models and providers do you work with?
We're provider-agnostic: OpenAI, Anthropic, Google, Groq, and self-hosted open-source models via Ollama. We routinely build routing layers that pick the right model per task — fast cheap models for classification, frontier models for reasoning — so you're never locked into one vendor's pricing or roadmap.
How do you keep AI features reliable in production?
The same way we run our own products: structured outputs with validation, fallback chains when a provider degrades, evaluation suites that run before every prompt change, cost and latency monitoring, and human approval queues for any action with real-world consequences.
What does an AI project typically cost?
AI pilots start from $9,500 and typically run 3–6 weeks: we pick one high-value workflow, ship it to production, and measure the impact before you commit to a broader rollout. Larger AI platforms are scoped like any product engineering engagement.
Can you add AI to our existing product?
Yes — most of our AI work is integration, not greenfield. We embed AI features into existing React/mobile apps and connect to your current backend and data, with guardrails so the AI never bypasses your existing permissions model.
How do you handle our data and privacy?
Your data never trains third-party models without your explicit decision. We default to zero-retention API configurations, redact PII before prompts where feasible, and can deploy fully self-hosted models for regulated workloads.
Let's Build Something Extraordinary
Whether you need an MVP in 6 weeks or an enterprise platform that transforms your industry — we're ready.