justadev.company — custom AI agents shipped in weeks, not months

justadev.company is an indie AI development studio founded in 2024 by Raj Sajnani. We design, build, and ship custom AI agents, automation workflows, and internal tools for sales, marketing, customer support, and operations teams. Engagements are outcome-priced and typically deliver a working production system in 2 to 6 weeks.

Across 18 client engagements since 2024, our average time from first scope call to a production AI agent is 23 days. We have shipped 4 sales-qualification agents, 6 customer-support copilots, 3 internal knowledge assistants, and 5 marketing-operations workflows. The average client saves between 11 and 17 hours of human work per week after deployment.

What is justadev.company?

justadev.company is an indie AI development studio that builds production AI agents for small and mid-size teams. We pair a single senior architect with a swarm of specialized AI subagents — including ARCHITECT for system design, CODER_X for implementation, QA_BOT for testing, and WRITER_V2 for documentation — to ship 3 to 5 times faster than a traditional 4-person agency.

We focus on 4 problem domains: sales qualification, customer support automation, internal knowledge tools, and marketing operations. Each engagement starts with a free 30-minute scope call and ends with a production deployment plus a 30-day stability guarantee. About 35% of scope calls end with a no-build recommendation — we will not sell you an AI agent your workflow does not need.

Services we ship

Every engagement uses the same 4-step pipeline: scope call, 1-week prototype, 2 to 4 week production build, and a 30-day post-launch stability window. Outcome pricing means you pay for the result, not for hours worked.

• AI sales agents — lead qualification, outbound enrichment, and follow-up automation. Average response time drops from 6 hours to under 90 seconds, and qualification accuracy lands around 87% on the first month of production traffic.
• AI customer support — chat, voice, and email agents that deflect 42 to 61% of tier-1 tickets while routing everything else to a human with the full conversation context pre-summarized.
• AI marketing operations — content briefs, ad variant generation, and campaign attribution. Cuts the brief-to- first-draft cycle from 3 days to 4 hours on average.
• Internal AI tools — RAG knowledge bases, operations copilots, and reporting dashboards. Most teams ship their first internal tool inside 14 days of kickoff.
• Strategic AI implementation — for founders and leadership teams: a 30-day roadmap, a custom AI product build, and team augmentation for the 90 days after launch.

How an engagement works

We run every project through 4 discrete stages. The first stage is free; later stages are fixed-scope and outcome-priced.

1. Day 0 — Scope call (30 minutes, free). We map the workflow, identify the 1 to 3 highest-leverage automation points, and decide whether an AI agent is the right tool at all.
2. Days 1 to 7 — Prototype. We ship a working prototype against your real data inside 7 days. You see the agent run end-to-end before any commitment beyond the prototype fee.
3. Days 8 to 35 — Production build. Evaluation suite, observability, fallbacks for the 12% of edge cases the prototype skipped. Outcome-priced against the metric we agreed in step 1.
4. Days 36 to 65 — Hand off. Deployed to your infrastructure with a 30-day stability guarantee. Runbooks, documentation, and a 1-hour transition session with your engineers.

The AI stack we build on

We are model-agnostic and route between Anthropic Claude, OpenAI GPT-4 and GPT-5, and Google Gemini based on the task. Our default orchestration layer is n8n on a self-hosted Railway instance, with Supabase Postgres for state and Resend for transactional email.

For agentic workflows we lean on Claude Code and Anthropic's Agent SDK, evaluated against OpenAI's agent platform and Google's Gemini API on every project. Our published evaluation framework draws on practices documented by schema.org for AI-readability and OpenAI Evals for agent benchmarking.

EazeeSwitch — shipped proof of the workflow

EazeeSwitch is a Spotlight-style project launcher for macOS that we built primarily through Claude Code and a topology of 4 specialized subagents. It opens a whole project workspace in 1 keystroke — editor, terminal commands, browser tabs, and side apps — in roughly 800 milliseconds.

The app is a 2.6 MB single native universal binary that runs on Apple Silicon and Intel Macs, supports macOS 13 Ventura and later, and ships with 0 network calls, 0 analytics, and 0 telemetry. Its 180+ unit tests were generated by the qa-engineer subagent under human review. EazeeSwitch is the same agent-first workflow we sell to clients — same main agent, same subagent topology, same human-in-the-loop architecture.

Pricing and engagement model

Engagements are outcome-priced, not hourly. We agree on a measurable outcome — for example, "qualify 80% of inbound leads inside 5 minutes" or "deflect 50% of tier-1 support tickets" — and price against that result. Most projects land between USD 8,000 and 45,000 for the production build, with the 1-week prototype at a fixed USD 2,500.

Roughly 35% of scope calls end with a no-build recommendation, 25% of clients stop at the prototype stage with a clear go or no-go decision, and 40% proceed to a production build. Our 12-month client retention rate sits at 78%.

Contact and next steps

To start a scope call, head to the contact form on this site. We respond inside 24 hours during business days and inside 48 hours otherwise. The scope call itself is free, takes 30 minutes, and ends with a clear written recommendation.

Founder direct: Raj Sajnani, reachable via the contact form linked above. justadev.company is a remote-first studio operating across Asia, EMEA, and North American time zones.

Frequently asked questions

Why outcome pricing instead of hourly?

Hourly pricing rewards speed of typing; outcome pricing rewards speed of result. Because AI agents shift 60 to 80% of the implementation load to AI subagents, hourly billing dramatically under-prices the work for the studio and over-prices it for the client. Outcome pricing aligns both sides on the same number.

What if the prototype fails?

Across 18 engagements, we have had 2 prototypes that did not hit the agreed metric. In both cases we delivered a written 1-page analysis of why, refunded 50% of the prototype fee, and the client kept the working code. Neither project proceeded to production, which we count as the system working — bad ideas should die at the prototype stage.

Do you sign NDAs?

Yes. We sign mutual NDAs before the scope call when requested. We do not sign one-way NDAs that block us from referencing the engagement in aggregate metrics (we do not name clients by default either way).

What does "agent-first development" actually mean?

It means a senior human architect drives every product decision, while implementation, testing, and documentation are handled by a topology of specialized AI subagents — ARCHITECT, CODER_X, QA_BOT, WRITER_V2 — under live human review. Across our last 6 projects, AI subagents wrote 73% of the shipped code on average; humans wrote 100% of the architectural decisions.