AI & Machine Learning

Production AI, Not Prototypes

Most AI consultancies ship demos. We ship systems — AI that runs in production with real users, monitoring, evaluation, cost control, and the governance your security team will actually sign off on.

See Our Work

For startups building AI-native products, SMBs automating the work that eats their margins, and enterprises that need AI implemented with security, compliance, and auditability.

Anyone Can Build an AI Demo. Almost Nobody Runs One in Production.

A demo has to work once, in a meeting. A production AI system has to work every time — for customers who phrase things badly, on data that changes daily, at a cost per interaction your CFO can live with, and under rules your compliance team can defend. That's what we build.

Evaluation before launch

We define what "correct" means for your use case and test against it, so quality is measured — not vibes.

Observability in production

Every prompt, response, cost, and latency is traced in Langfuse. When something drifts, we see it before your users do.

Cost control

Model routing, caching, and prompt design that keep per-interaction costs predictable as usage scales.

A feedback loop

Human review of real interactions feeds back into prompts, retrieval, and evaluation sets — so the system improves after launch instead of decaying.

AI Services That Ship

AI integration into existing products

Add AI capability to the software you already run — without rebuilding it or breaking your data model.

RAG on your private data

Retrieval-augmented generation grounded in your documents and records — answers come from your data, and your data stays in your cloud account.

AI voice agents

Voice agents that answer inbound calls, qualify callers, answer questions, and book appointments — then hand off to a human when a call needs one.

AI sales assistants

Assistants that engage leads in natural conversation, answer from your actual inventory and knowledge base, and move buyers toward a booked appointment.

Agent workflows

Multi-step AI agents that execute business processes — reading, deciding, calling your APIs, and escalating to a person when confidence drops.

Evaluation & observability

Langfuse-based tracing, scoring, and evaluation pipelines — for systems we build, or for AI you've already deployed and can't currently measure.

Built on the Platforms Enterprises Already Trust

We deploy on the AI infrastructure your cloud and security teams have already approved — not a stack of startup APIs that changes under you.

Amazon Bedrock

Claude and Cohere models inside AWS — private data never leaves your cloud boundary.

Azure AI Foundry

For organizations standardized on Microsoft. Multi-cloud so your strategy drives the architecture.

pgvector on Aurora

Vector search inside the managed database you already operate — no separate vector vendor.

Langfuse

LLM observability and evaluation: full traces, cost tracking, quality scoring, eval datasets.

AI Your Security Team Can Say Yes To

Enterprise AI projects don't die in the demo — they die in security review. We design for that review from day one.

AI governance

Documented model choices, prompt versioning, and change control — you can answer "what is the AI doing and who approved it" at any point.

Data privacy

Your data stays in your cloud account. Private-data workloads run on Bedrock or Azure AI Foundry inside your boundary — never used to train anyone else's model.

Role-based access control

AI features respect the same permissions as the rest of your app. Users can't retrieve through the AI what they couldn't see in the UI.

Audit trails

Every AI interaction is logged and traceable — who asked, what was retrieved, what was answered, and what it cost.

Human-in-the-loop review

For consequential actions, a person approves before the system acts. Confidence thresholds and escalation paths are designed in, not promised later.

We build CJIS-compliant systems running in AWS GovCloud and carry SOC 2 and ISO 27001 roadmaps in progress — the same security discipline applies to every AI system we ship.

QStart Labs is playing a key role in assisting Greif's expansion into new lines of business through the use of technology. Their approach has allowed us to quickly bring value to our customers while laying out a technology roadmap aligned with our long-term objectives. The fast paced success we are experiencing is due to their strong strategic planning, detailed work processes, and pragmatic approach to technology development.
David FischerPresident, Greif

AI FAQ

Most stalled pilots fail for the same three reasons: no definition of "correct" (so quality arguments never end), no observability (so nobody can say what it costs or where it fails), and no path through security review. We start with those three. Evaluation criteria are defined before we build, every interaction is traced in Langfuse, and governance is designed for your security team's checklist — which is what turns a pilot into a deployment decision.
Usually the one your cloud and security teams have already approved. On AWS, Amazon Bedrock gives you Claude and Cohere models without data leaving your account. On Microsoft, Azure AI Foundry does the same inside Azure. We're deliberately multi-cloud so the recommendation fits your constraints, not our reseller agreement — and we design so the model can be swapped as the market moves.
No. We run private-data workloads through Bedrock or Azure AI Foundry inside your own cloud boundary, where provider terms exclude training on your data. Retrieval indexes live in pgvector on your Aurora PostgreSQL instance. Your data stays your data — a design requirement, not a configuration afterthought.
Yes, if it's architected for that from the start. We build CJIS-compliant systems in AWS GovCloud and design AI features with role-based access, audit trails, encryption, and human-in-the-loop review. It's the same discipline we apply on our Security & Compliance engagements — AI doesn't get an exemption.
Because it's measured. We instrument every system with Langfuse: full traces of prompts and responses, cost and latency per interaction, and quality scores against evaluation sets built from your real traffic. You get a dashboard, not a shrug. When quality drifts, it shows up in the numbers before it shows up in complaints.
It depends on three drivers: how much is integration with existing systems, how consequential the AI's actions are, and how much data preparation your content needs. A scoped first release is typically weeks to a few months. The AI Discovery Call exists to give you a real number for your case — see the Pricing Guide for ranges.

Put AI in Production — With Proof It Works

Bring us the use case. We'll tell you what it takes to ship it, run it, and defend it in security review.

Your information is kept private and will never be shared.

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hello@qstartlabs.com(614) 768-3887
6233 Riverside Drive, Suite 2S, Dublin, OH 43017 (Columbus metro) · serving clients nationwide

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