Production-Ready AI Development for High-Growth Startups
We build AI systems that survive contact with production traffic — not a notebook demo. Machine learning, generative AI, LLM applications, and autonomous agents, engineered by people who own the on-call rotation.
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What AI Development Covers
Machine Learning
Custom models for prediction, classification, and ranking, trained on your data and evaluated against your actual success metric.
Generative AI
Content, code, and design generation systems built with grounding and guardrails, not an unmoderated model call.
LLM Applications
Production apps on top of Claude, GPT, and open models — RAG, structured output, tool use, and evaluation harnesses included.
AI Agents
Autonomous agents with sandboxed reasoning, auditable decision paths, and a defined human-handoff point.
Built for Specific Buyers, Not Everyone
Series A–C Founders
Need AI shipped into the core product this quarter, not a research spike.
CTOs Replacing a Stalled POC
Have a proof of concept that worked once and needs to survive real users and real data.
Product Leads Scoping AI Features
Know the outcome they want but need an engineering partner to define the actual system.
If you're looking for a slide deck on "AI strategy" with no build attached, we're the wrong partner — we scope by writing the system, not the pitch.
From Kickoff to Launch
Scoping & Data Audit
We map your data sources, success metric, and failure tolerance before any model gets chosen.
→ Scoping document + architecture recommendationProof of Concept
A narrow, working slice against real data, evaluated against the metric agreed in scoping.
→ Working PoC + evaluation resultsProduction Build
Full system build: pipeline, model or LLM integration, monitoring, and the application layer around it.
→ Deployed system in your infrastructureLaunch & Handoff
Monitoring dashboards, runbooks, and a walkthrough with your team before we step back.
→ Documented, monitored, owned systemTechnologies We Use
“Northell didn't just build a model — they built the pipeline, the eval harness, and the on-call runbook around it. Nobody else scoped it that way.”
Common Questions About AI Development
What AI development services does Northell offer?
Machine learning, generative AI, LLM application development, AI agents, RPA and intelligent automation, plus AI strategy and integration.
How long does an AI project take?
A focused proof of concept typically takes 4–6 weeks; production builds run 3–6 months depending on data readiness and scope.
Do you work with our existing data infrastructure, or do we need to migrate first?
We build against what you have. Most engagements start with a data-readiness review so scope reflects your actual pipelines, not an idealized version of them.
Which models do you build on?
We're model-agnostic — Claude, GPT, and open-weight models — and choose per use case based on cost, latency, and data-handling requirements, not a default vendor relationship.
What happens after launch — do you handle monitoring and retraining?
Yes. Every production AI system we ship includes monitoring for drift and failure modes; ongoing MLOps and retraining are available as a follow-on engagement, not bundled by default.
Can you take over a stalled AI project from another vendor or an in-house team?
Regularly. We start with an architecture and data audit to find why it stalled before writing new code — usually it's a scoping or evaluation-harness gap, not a model problem.