AI agents & autonomous workflows
Multi-step systems that take actions on your behalf: research, drafting, triage, tool use.
Works well for: sales prospecting, customer support tier-1, internal ops automation.
AI Development Services
AI development services that ship production AI
We build custom AI integrations, agents, and workflows for teams that need software in production — not slide decks. 60+ projects shipped. 30+ engineers. Fixed-price scopes after a free discovery call.
We’re an engineering team that does AI work — not a consultancy that talks about it.
Every project ends with code in production, monitoring in place, and a runbook your team can read.
What are AI development services?
AI development services build production AI systems for businesses that don’t have in-house machine learning teams. That means scoping the right use case, choosing the right models and tools, integrating with the software you already run, and shipping to real users — not writing white papers or running workshops.
At NerdHeadz, AI development isn’t a consulting category. It’s engineering work that ends with software in production.
How we’re different:
- vs. a freelancer — you get an engineering team, internal tooling, and a process refined across 60+ shipped projects, not one person’s calendar.
- vs. an in-house hire — you pay for the project scope, not a six-month salaried runway with no guarantee of delivery.
- vs. enterprise “AI transformation” consultancies — we ship code. Our stack is Claude Code, TypeScript, Python, React, Next.js, and the AI APIs that best fit your problem.
How we deliver AI automation projects
Every project follows a four-phase cycle. The weight of each phase depends on how well-defined the problem is when you arrive.
1
Discovery
1–2 weeksWe learn the business problem, review your data and systems, and identify the decision or task the AI needs to inform or automate. AI projects fail most often here — the wrong problem getting scoped. You get: a scope document and a fixed-price quote.
2
Prototyping
1–2 weeksWe build a coded proof-of-concept against your real data — not a demo on synthetic inputs. If the output isn’t good enough, we tell you before the production build starts. You get: a working prototype and an honest go/no-go.
3
Build
3–8 weeksProduction integration with your software, human-in-the-loop safeguards, monitoring for drift and failure cases, deployment to your infra or ours. You get: a production system, monitored and documented.
4
Handoff
1 weekProduction deployment, monitoring dashboards, runbooks for common failure modes, and documentation engineers who weren’t on the build can read. You get: the keys, the docs, and a maintenance option.
End-to-end6–12 weeks
What we actually build
RAG & knowledge systems
Retrieval-augmented generation over your internal data: docs, tickets, contracts, call transcripts.
Works well for: support deflection, sales enablement, internal Q&A, research synthesis.
AI chatbots & conversational interfaces
Production chatbots with grounded answers, escalation paths, and analytics.
Works well for: lead qualification, onboarding, support, internal assistants.
Document & data extraction
Pulling structured data out of PDFs, emails, contracts, invoices, forms.
Works well for: finance ops, legal review, claims processing, KYC.
Content & code generation
AI-assisted content pipelines with human review, plus code-generation tooling for internal teams.
Works well for: content marketing teams, dev productivity, localization.
Custom LLM integrations
Embedding Claude, OpenAI, or open-source models into existing apps without rebuilding them.
Works well for: SaaS products adding AI features, legacy systems getting AI assistants.
When AI automation actually delivers ROI
AI works well for a narrow set of problem shapes — and fails predictably on others. Here’s the honest breakdown.
✅ Works well
❌ Usually doesn’t work
Task shape
High-volume repetitive tasks where judgment is light — classification, extraction, triage, tier-1 support, content with review. Throughput up 5–10×.
Replacing expert judgment entirely. AI is an assistant, not a replacement — pretending otherwise is how AI projects get abandoned.
Data
Surfacing insights from unstructured data your team can’t read fast enough — ticket trends, call summaries, monitoring, research synthesis.
High-stakes decisions without review — medical, legal, hiring; anywhere errors are expensive and accountability matters.
Goal
Decision support where a draft with reasoning beats a blank page — drafting, summarizing, explaining, translating.
“AI transformation” as the goal. Transformation is marketing language, not a problem. We build AI into specific workflows.
Before we commit, we tell you which category your use case falls into. If it’s in the “doesn’t work” bucket, we say so. We’d rather lose the contract than ship you an AI system that fails in production.
The stack we build on
We pick the right tool per project. These are the ones we reach for most.
AI models & APIs
- Claude (Anthropic)
- OpenAI
- fal.ai
- Groq
- Hugging Face
Languages & frameworks
- TypeScript
- Python
- Next.js
- FastAPI
- React Native
Engineering velocity
- Claude Code
- Cursor
- GitHub
- Selfware tooling
Why these? Supabase with pgvector keeps RAG simple — embeddings sit in the same Postgres tables as your relational data. FastAPI is the Python sidecar pattern we use when AI work needs the Python ML ecosystem from a Node app.
We ship AI into these industries
Proof
AI projects we’ve shipped
AI build
AI · venture intelligence
PovNexus
A B2B SaaS venture-intelligence platform — multi-hypothesis AI agent reasoning grounded in vector, SQL, and live-web tools.
OpenAIAnthropicSupabase
Read case study →
AI build
AI · SaaS productization
SmartHostingHub
Eight ChatGPT custom GPTs rebuilt as a production AI SaaS for Airbnb hosts — free listing analyzer, Stripe billing, live in ~6 weeks.
Next.jsClaudeStripe
Read case study →
AI build
AI · Voice
AI Call Center
Live-call transcription, summarisation, and routing built on a production voice + LLM pipeline.
Read case study →
AI build
AI · SaaS
FutureSpark
AI product features shipped into a live SaaS platform with human-in-the-loop review.
Read case study →
AI build
AI · Vision
AI InteriorFlow
Generative AI workflow turning room inputs into structured design output.
Read case study →
Proof · client voices
And it works, every time.
Hear it straight from our customers.
01 / 07
This system has been a dream of mine for almost a year. I have tried to build it myself and finally came to the conclusion I needed help. The NerdHeadz team has built me exactly what I was dreaming about and more! Working with them has been an absolute pleasure. I can't thank them enough.
Amy Olson
Founder & Airbnb Listing Strategist, Smart Hosting Hub
3+
Years of industry leadership
30+
Experts ready to build
60+
Projects delivered on time
90%
Client retention
✺Why NerdHeadz
Why teams pick NerdHeadz for AI work
01
Engineers, not consultants.
Every person on your project writes code. No account-manager layer between you and the people building.
02
Honest about fit.
We tell you when AI is the wrong answer. ~20% of scoping calls end with us recommending a non-AI solution or a different agency.
03
Fixed-price scopes.
After discovery, you get a fixed price. No T&M surprises during the build.
04
Selfware handoff.
We hand you a system your team can extend with AI tools — Claude Code, Cursor, our internal templates. You’re not locked in.
✺FAQ
Frequently asked questions
Custom AI tools and platforms: LLM integrations, RAG systems, AI agents, chatbots, document and data extraction pipelines, computer vision, and intelligent workflow automation. We work with both startups shipping their first AI feature and B2B SaaS teams adding AI to mature products.
Most AI projects take 6-12 weeks end-to-end: 1-2 weeks discovery, 1-2 weeks prototyping, 3-8 weeks build, 1 week handoff. Simpler integrations (a single AI feature into an existing app) can ship in 3-4 weeks.
We pick the right model per project. Most often: Claude (Anthropic), OpenAI GPT models, and open-source LLMs via Groq. For infrastructure: Python, TypeScript, FastAPI, Next.js, LangChain, PostgreSQL with pgvector, Supabase. We are not locked into any vendor - we pick what fits your problem and your budget.
Yes - that is most of what we do. We add AI features to existing platforms (search, content generation, recommendations, automation) without breaking what is already working. Common starting points: an AI assistant inside your SaaS app, AI-powered search over your content, or an automation layer around your existing workflows.
Most projects fall between $15,000 and $150,000+ depending on scope, data complexity, and integration depth. After discovery you get a fixed-price quote - no T&M surprises. Smaller Selfware AI tools run $3,000-$15,000 over 2-4 weeks.
If you want us to. We offer post-launch maintenance at a lower T&M rate, or hand off entirely with runbooks and documentation your team can read. Most clients keep us on for the first 3-6 months, then move to in-house or ad-hoc.
Yes. All code is yours. All prompts, evaluation suites, and infrastructure-as-code is yours. Where we use third-party APIs (Claude, OpenAI), you own your account and billing relationship.
Let’s ship
Ready to scope your AI project?
Tell us about the workflow, decision, or product you want AI to power. We’ll come back with a feasibility take, a recommended approach, and a fixed-price quote.