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.
Written by Raj Sajnani,
Founder and AI Solutions Architect at justadev.company.
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.
-
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.
-
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.
-
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.
-
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%.
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.