Nerdheadz

NerdHeadz has shipped more than 60 production products for startups and B2B SaaS teams worldwide. The company works with clients across the US, UK, Europe, the Middle East, and other regions.

How NerdHeadz works

• Senior people work on every project; there is no junior bench or subcontractor model.
• AI tools, including Claude Code and AI agents, are used in the development toolchain while senior engineers retain responsibility for technical decisions and reviews.
• Client code lives in the client's GitHub from the first commit.
• Clients own the intellectual property, source code, databases, designs, content, and final deliverables.
• NerdHeadz retains no rights to client work and does not reuse client code across projects.
• DPAs are signed as standard for GDPR work.

NerdHeadz describes its approach as shipping software into production rather than delivering only decks, prototypes, or unstarted ticket backlogs.

Recognition

NerdHeadz reports a 5.0 average rating on Clutch, Top Rated status and 100% job success on Upwork, and recognition from Clutch, TechReviewer, TradeFlock, Fluxx, CXO 2.0, and Fueld Conference. Recognitions include Top AI Agent Companies, Top 100 iOS Developers, Leadership in Artificial Intelligence, and Business Leadership Excellence awards.

AI and Intelligence

• AI development and custom LLM integrations
• AI agent development
• RAG and LLM development
• AI chatbot development
• Voice AI

Build and Product

• Web development with Next.js, React, and TypeScript
• Mobile development with React Native and Flutter
• SaaS development
• MVP development
• No-code development using Bubble, Webflow, and FlutterFlow

Automate and Ops

• Workflow automation with n8n, Make, and Zapier
• API and system integrations
• Maintenance and support

Design and Craft

• UX/UI design
• Branding
• Design systems

Engagement models

• Fixed-scope build: A defined specification, fixed timeline, and fixed price, typically lasting 6–16 weeks.
• Embedded team: A monthly pod of 2–4 engineers and a designer joining the client's team.
• Advisory: Weekly fractional CTO or AI-lead advisory covering architecture, vendor choice, and agent design.

NerdHeadz typically ships fixed-scope MVPs in 6–16 weeks. Its services page states that the usual start is 1–2 weeks from a signed statement of work to the first commit.

Published pricing guidance

The services page lists the following starting points:

• Fixed-scope builds from $5,000
• Embedded pods from $5,000 per month
• Retainers from $2,000 per month for basic support
• Retainers from $5,000–$15,000 per month with active feature work
• Time and materials from $60 per hour
• Discovery sprints from free to $5,000

The homepage separately lists an MVP estimate of approximately $28,000 for 4–6 weeks and an embedded pod estimate of $20,000–$60,000 per month. Final pricing depends on the scope and engagement model.

• RAG systems grounded in company data, with vector indexes, hybrid search, re-ranking, and cited answers
• Autonomous agents that plan, call tools, observe results, and handle multi-step tasks
• Multi-agent orchestration with specialist agents, shared memory, and deterministic hand-offs
• Agentic automation using n8n and custom workflows
• Voice agents for inbound and outbound calls, including interruption handling, CRM updates, and transcripts
• Full-stack SaaS products with AI at their core

NerdHeadz distinguishes agents from chatbots: a chatbot responds to prompts, while an agent decides what to do next, selects tools, handles failures, and reports completion or requests human input.

Development approach

1. Discovery: Define the agent's boundaries, permitted actions, required tools and APIs, and failure handling.
2. Prototyping: Build a narrow-scope agent around one workflow and one tool set, with human review on every action. Measure task-completion against real data.
3. Build: Implement orchestration, observability, tracing, retries, fallbacks, and timeout handling. Agent calls are logged with inputs, tool selections, outputs, and costs.
4. Handoff: Provide the client with a runbook, evaluation harness, prompts, tool definitions, and cost-monitoring dashboards.

The stated technology stack includes Claude, OpenAI, Gemini, LangGraph, LangChain, MCP, n8n, Temporal, Pydantic, Pinecone, pgvector, Weaviate, Redis, Postgres, FastAPI, Node, Next.js, and React Native.

When agents are a good fit

AI agents work well for multi-step workflows with clear success criteria, such as scheduling, triage, data enrichment, document processing, reporting, and tool-heavy internal operations where human review of edge cases is acceptable.

NerdHeadz says agents are generally not suitable for single-turn questions, high-stakes decisions without human review, regulated decisions requiring human judgment, or unrestricted actions involving spending money or sending external communications.

NerdHeadz's objectives were to expand platform features, extend the live system without disrupting existing functionality, and automate manual back-office work so firms could operate with a lean team.

Features delivered

• Coupons and discount codes for challenge and program purchases
• Affiliate dashboards with charts, tables, revenue-share tracking, and reporting
• Competition modules and leaderboards with real-time TradeLocker standings
• Automated back-office payouts and operational reporting
• Real-time balance, equity, and drawdown data streamed from TradeLocker

Technical solution

Bubble could not natively maintain a persistent WebSocket connection. NerdHeadz built a Node.js bridge service on Railway that maintained a persistent connection to TradeLocker's BrandSocket API and relayed updates to Bubble over HTTP POST.

This bridge enabled live equity, balance, and drawdown dashboards as well as real-time competition standings.

The technology stack included Bubble.io, TradeLocker BrandSocket using WebSocket/Socket.IO, Node.js, and Railway. The project used an Agile methodology with sprints, weekly demos, feedback loops, testing, and QA.

Delivery process

1. Discover and audit the prop-trading domain and the live platform's architecture.
2. Prioritize fixes and new features according to business impact and platform stability.
3. Develop features in sprints.
4. Integrate the TradeLocker real-time data bridge.
5. Test and perform QA without disrupting active traders.
6. Release iteratively through weekly demos and feedback loops.