AI built for production, not the demo stage
LLM apps, RAG, agents, fine-tuning, evals, and classic ML—delivered as real products with monitoring, cost controls, and rollback paths. We integrate with your engineers and ship alongside your roadmap.
For the full AI/ML capability list, industry use cases, and our step-by-step project questionnaire, see AI & machine learning solutions — this page is the fast “how we work” story.
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What we build
End-to-end delivery—from scoping and architecture to production monitoring. Aligned to how real AI products get evaluated and maintained.
LLM Apps & Integrations
Chat interfaces, copilots, and workflow UIs on top of frontier APIs or private deployments—with streaming UX, rate limits, retries, and cost controls baked in.
RAG & Knowledge Systems
Chunking strategies, embedding pipelines, vector stores, hybrid search, re-ranking, and citation UX—so answers stay grounded in your documents and stay current.
Agents & Automation
Tool-calling, multi-step orchestration, human-in-the-loop approvals, and guardrails for support, ops, research, and back-office workflows that need to act—not just answer.
ML & Predictive Models
Forecasting, classification, anomaly detection, and recommendation—deployed with monitoring, retraining triggers, and clear data ownership between data science and engineering.
Evals & Quality Assurance
Automated eval suites, regression harnesses, prompt-version tracking, and latency/cost dashboards—so you can ship model updates without flying blind.
Fine-tuning & Specialization
Domain adaptation, instruction tuning, and RLHF-adjacent techniques where fine-tuning beats prompting for cost, latency, or consistency—with a clear eval baseline first.
Work we've shipped
Production systems—not slide decks or demos.
NutriAnalytics — yield prediction & soil intelligence
Multi-modal data pipelines ingesting satellite imagery, IoT sensor readings, and historical yield data. ML models delivering field-level recommendations to agronomists and growers across North America.
TimeOff Manager — HRIS & intelligent scheduling
Full-stack HR platform with automated approval workflows, policy-aware scheduling logic, and conflict detection. Built to plug into existing payroll and identity stacks without custom middleware.
Mining & mineral exploration — predictive targeting
Integrating geochemical assay data, geophysical surveys, and drill-log records into ML models that surface high-probability target zones. Infrastructure designed for field-grade reliability and geologist-friendly UX.
How we work
The same accountable delivery process we use for web and mobile—applied to AI. No "we'll figure it out in sprint 7."
Discover
Goals, users, data sources, risk tolerance, and success metrics—before touching a model or writing a line of code.
Design
Architecture, eval plan, API contracts, data flows, security review, and a thin vertical slice to validate value fast.
Build
Iterative delivery with eval-driven quality gates. Security hardening and load testing before production traffic hits.
Operate
Monitoring dashboards, latency and cost alerts, model version management, and iteration as your data and users evolve.
Common questions
Straight answers for teams evaluating an AI build partner—not a sales page.
How long does an AI MVP take?
Most scoped MVPs land between a few weeks and a few months depending on data readiness, integrations, and compliance requirements. We start with a 30-minute scoping call and deliver a written plan with milestones before writing production code. You'll know the timeline before you commit to anything.
What does AI development cost?
Scope drives cost more than technology. A lean first-slice integration (existing API, narrow workflow) can be scoped in days and built in weeks. A production RAG or agent system with evals, security hardening, and monitoring is typically a multi-month engagement. We share a written estimate with milestones after the scoping call—no charge.
We already have in-house engineers—where do you fit?
We work alongside your team rather than replacing it. Common patterns: we own a specific capability (RAG pipeline, eval harness, agent orchestration) while your team owns the product shell; or we embed for a sprint to upskill and hand off. We document everything and prefer your repo over ours.
Which LLM providers do you integrate?
We routinely work with OpenAI, Anthropic, Azure OpenAI, and Google Vertex AI, alongside open-weights models (Llama, Mistral, etc.) where cost, latency, or data residency make sense. Model selection is driven by your requirements—not by what's generating the most hype this week.
What is RAG and when do we actually need it?
Retrieval-Augmented Generation grounds model answers in your documents or structured data instead of relying on training-time knowledge. You need it when answers must cite internal or up-to-date sources, when hallucination in your domain is costly (legal, medical, ops), or when you want users to trust—and verify—what the model says.
Do you work with our data security and compliance requirements?
Yes. We design around your data retention, residency, access control, and vendor policies—whether that means private cloud, VPC deployments, or enterprise SSO. We document data flows and model usage before any implementation work starts.
Can you help after launch?
Yes. Ongoing support includes monitoring hooks, eval dashboards, prompt and model version management, cost/latency alerting, and iterative improvements as your traffic and feedback grow. We can also train your team to own it themselves—your call.
How do we get started?
Book a free 30-minute scoping call below, use Build Your App to describe your project in writing, or go through the AI/ML questionnaire on our solutions page. Either way you'll get feasibility, stack recommendations, and a high-level timeline—no pitch deck unless you ask for one.
Ready to scope your AI product?
Tell us about your users, data, and the outcome you're after. We'll map a pragmatic path to production—written plan, milestones, and honest fit assessment included.
No pitch deck. No retainer required to start the conversation. Calendar opens in one click.