Outsourced AI Development

Outsourced AI Development That Actually Ships

Build AI systems at 40–60% lower cost than hiring. Production-ready systems, senior expertise, predictable costs—without the recruiting, retention, and ramp-up headaches.

Get a Cost Comparison See How We Work

Who This Is For

Companies that need AI without the overhead

Startups

That need AI features but can't afford $400K+ annually for a small ML team

Scale-ups

With proven product-market fit who need to add AI without slowing core development

Enterprises

With AI initiatives stuck in planning because internal teams are at capacity

Technical Founders

Who understand AI but don't have time to build it themselves

The Problem

Why in-house AI teams fail

The math on building an internal AI team rarely works:

$50K–100K

per engineer in recruiting fees, lost productivity, and onboarding

$180K–300K

base salary expected by senior ML engineers plus equity and benefits

18 months

average retention—you're constantly rebuilding institutional knowledge

$100K+

annual infrastructure overhead for ML tooling, compute, and DevOps

For most companies, the first 12 months of an internal AI team produces more planning documents than production systems.

The Alternative

What outsourcing gets you

Immediate capacity — No recruiting. Start building in week one.

Senior expertise from day one — No ramp-up period. We've shipped these systems before.

Predictable costs — Fixed-price milestones or monthly retainers. No surprise headcount growth.

Reduced management overhead — One relationship to manage, not a team of specialists.

Built-in redundancy — If someone's sick or leaves, your project doesn't stop.

What We Build

Outsourced AI development services

Machine Learning Systems

Predictive models, classification, anomaly detection, forecasting

Natural Language Processing

Text extraction, summarization, sentiment analysis, search

Computer Vision

Image classification, object detection, OCR, visual inspection

AI Automation

Workflow engines, document processing, decision automation

LLM Applications

Custom chatbots, RAG systems, AI assistants, content tools

Data Infrastructure

Pipelines, feature stores, model serving, monitoring

How We Work

Structured delivery, predictable results

Discover

Week 1–2

Technical scoping with your team. Define success, identify integration points, create milestone roadmap.

Build

Week 3–8

Iterative development with weekly demos. You see progress, not just status reports.

Ship

Week 8–10

Production deployment, integration testing, documentation, and team training.

Scale

Week Ongoing

Optional retainer for maintenance, iteration, and expansion.

Cost Comparison

In-House vs. Outsourced: The Numbers

Cost Factor	In-House Team (3 engineers)	Outsourced Partner
Year 1 fully-loaded cost	$600K–900K	$150K–400K
Time to first production system	6–12 months	6–12 weeks
Recruiting and onboarding	$50K–150K	$0
Infrastructure and tooling	$50K–100K	Included
Management overhead	10–20 hrs/week	2–4 hrs/week
Risk if key person leaves	Project stalls	Continuity guaranteed

Costs vary by project scope, geography, and seniority requirements.

The bottom line: Outsourcing delivers production AI at 40–60% lower total cost with 4–6x faster time to value.

Typical Outcomes

What clients achieve with outsourced AI

$300K–600K

saved in year one vs. building internal team

8–12 weeks

from kickoff to production deployment

Zero

recruiting overhead—start with senior engineers immediately

30–50%

of internal team time freed by automating manual work

Get AI Capabilities Without the Headcount

You need production AI systems, not an internal team to manage. Let's talk about what you're building and whether outsourcing makes sense for you.

Schedule a Technical Call

30-minute call with an engineer. No sales pitch—just a real conversation about your project.