Cosellr
Rapid MvP / Cloud Native Development / Developer Enablement / Platform Engineering / GCP / AWS
Executive brief
From zero to AI-first sales platform in 8 weeks
Cosellr was a net-new build with an aggressive timeline: deliver a real, usable product—not a prototype—capable of running an end-to-end sales workflow. We engineered the MVP around core product pillars (automated prospecting, hyper-personalized outreach, automated nurturing, and in-call agentic AI) while building production-grade foundations from day one: security controls, observability, CI/CD, and scalable architecture. The outcome was a launch-ready system designed to iterate quickly post-release without accumulating early-stage fragility.
Time to MVP (net-new build)
8 weeks
end-to-end delivery
Core workflow coverage
4/4 pillars shipped
within 8 weeks
Time-to-first-value target (new user)
≤ 30 minutes
post-launch target
Shipped a net-new production MVP in 8 weeks, spanning prospecting → outreach → nurturing → meeting outcomes, with a cohesive end-user workflow.
Delivered core product pillars: automated prospecting, hyper-personalized outreach, automated nurturing, and real-time in-call agentic AI support.
Implemented natural-language onboarding and campaign setup flows to reduce setup friction and improve time-to-first-value.
Established a production-ready foundation: CI/CD, logging, metrics, error reporting, and deployment playbooks to support fast iteration post-launch.
Engagement shape
Duration
8 wks
Phases
7
Deliverables
8
Time to MVP (net-new build)
8 weeks
How it ran
Sprint-driven build with an end-to-end workflow shipped early, then hardened weekly.
We built Cosellr’s AI-first sales platform from scratch in 8 weeks—launching a production-ready MVP across prospecting, outreach, nurturing, and in-call agent assistance.
Starting position
What Cosellr was working against.
Cosellr needed a net-new MVP that could demonstrate a complete ‘contact to contract’ workflow: identify prospects, generate outreach, nurture leads, and support the sales cycle through meetings. There was no legacy system to extend and no baseline usage to optimize against—so the challenge was shipping a coherent, production-ready foundation quickly, while leaving room for fast iteration and feature expansion post-launch.
Constraints we worked inside
5 in playEvery one of these ruled an easier option out. They are the reason the approach looks the way it does.
Hard delivery window: production MVP in 8 weeks.
Net-new product: no existing baseline, no legacy architecture, no mature ops environment.
AI features must be safe, controllable, and consistent (avoid “demo drift”).
Core workflows must feel integrated (not separate tools stitched together).
Engineering must support rapid iteration after launch (tight release cycle, observability, and rollback readiness).
What had to be true to finish
5 goalsAgreed up front, so the engagement could be called finished on evidence instead of opinion.
Ship a complete, usable MVP spanning prospecting → outreach → nurturing → meeting support.
Deliver differentiating AI features: automated prospecting, hyper-personalized outreach, and real-time in-call agentic assistance.
Reduce onboarding friction through guided/natural-language setup flows.
Stand up production-grade foundations (CI/CD, observability, security) so the MVP can scale post-launch.
Ensure the architecture supports adding new workflows, agents, and channels quickly.
Industry context
Rapid MvP
Cloud Native Development
Developer Enablement
Platform Engineering
GCP
AI-first sales platforms must combine reliable automation with real-world usability: onboarding, targeting, messaging, inbox monitoring, scheduling, and meeting outcomes. For a net-new product, the challenge is shipping a cohesive end-to-end workflow quickly—without creating a brittle demo that can’t scale.
Approach
How the work was sequenced.
We executed an 8-week, sprint-driven build focused on (1) shipping an end-to-end workflow users could complete, (2) establishing a production-grade foundation from day one, and (3) ensuring AI features were implemented as reliable systems—tool-driven, observable, and controllable—rather than prompt-only demos.
Delivery track
Segment width reflects the number of workstreams inside each phase — where the engagement actually spent its effort.
Phase 1 — Product blueprint + end-to-end workflow skeleton (Week 1)
3 workstreams
Defined the MVP ‘happy path’: onboarding → connect channels → define targeting → launch outreach → nurture → schedule meetings → track outcomes.
Specified product pillars and minimum viable UX for each step so the workflow felt cohesive.
Set operational requirements early (logging, metrics, error reporting, basic dashboards) to avoid “invisible failures.”
Phase 2 — Prospecting + targeting foundations (Weeks 2–3)
3 workstreams
Implemented automated prospecting workflows aligned to ICP targeting.
Added targeting inputs and configurable constraints to ensure prospect selection stayed relevant.
Created data contracts for prospect profiles and research outputs to keep downstream steps consistent.
Phase 3 — Outreach + hyper-personalized messaging (Weeks 3–4)
3 workstreams
Built outreach generation workflows designed to produce natural, context-aware messages at scale.
Implemented message templating and structure so teams could maintain brand voice and compliance.
Introduced safeguards to prevent off-brand outputs and reduce hallucinated claims (grounding + constraints).
Phase 4 — Nurturing + inbox monitoring loop (Weeks 4–5)
3 workstreams
Implemented automated nurturing logic so leads don’t fall through cracks (open/no reply, delayed clicks, re-engagement patterns).
Added monitoring/trigger infrastructure to detect prospect signals and queue follow-ups.
Ensured nurturing actions remained explainable (why this follow-up, why now).
Phase 5 — Sales-cycle support + meeting outcomes (Weeks 5–6)
3 workstreams
Built meeting scheduling flows and outcome capture patterns.
Implemented meeting support primitives (recording/transcription hooks, summaries, next steps) as a foundation for advanced in-call assistance.
Defined structured ‘post-meeting artifacts’ to help close loops reliably (recap, tasks, follow-up drafts).
Phase 6 — In-call agentic AI + natural-language onboarding (Weeks 6–7)
3 workstreams
Added real-time agentic assistance during calls (respond to commands, pull context/resources, generate support artifacts).
Implemented natural-language onboarding flows to reduce setup friction and improve time-to-first-value.
Applied guardrails to ensure safe tool usage, consistent behavior, and predictable outputs.
Phase 7 — Production hardening, QA, and launch readiness (Week 8)
3 workstreams
End-to-end testing across the full workflow: onboarding → outreach → nurture → meetings → follow-ups.
Performance and reliability pass: retries/timeouts, cost controls, error handling, and fallback behaviors.
Release readiness: CI/CD pipelines, deployment runbooks, monitoring/alerting baselines, and rollback plan.
What the team kept
Artefacts handed over at the end of the engagement — owned and operable by Cosellr without us.
Production MVP of Cosellr’s AI-first sales platform (end-to-end workflow)
Automated prospecting + targeting configuration system
Hyper-personalized outreach generation + message structure templates
Automated nurturing engine with inbox monitoring triggers
Meeting scheduling + outcome capture + follow-up generation primitives
In-call agentic AI foundation + natural-language onboarding flows
CI/CD pipelines + environment configuration + deployment runbooks
Observability baseline (logs/metrics/errors) + operational playbooks
Results
What actually moved.
Because this was net-new development, success was measured by time-to-market, completeness of workflow coverage, production readiness, and the platform’s ability to support iteration immediately after launch. The MVP shipped in 8 weeks with a cohesive end-to-end workflow and a production-grade engineering foundation, enabling immediate user onboarding and continued feature expansion without rewrites.
Outcome ledger
Every figure we measured on this engagement, including the ones that are ranges rather than headlines.
Time to MVP (net-new build)
From greenfield start to production-ready MVP.
8 weeks
end-to-end deliveryCore workflow coverage
Prospecting, outreach, nurturing, and sales-cycle/meeting support shipped as a single cohesive system.
4/4 pillars shipped
within 8 weeksTime-to-first-value target (new user)
Estimated onboarding goal: connect channels + define target + launch first campaign. Replace with product analytics later.
≤ 30 minutes
post-launch targetProduction readiness checklist completion
Estimate: CI/CD, observability baseline, runbooks, and rollback readiness established early. Replace with internal checklist stats.
80–90%
by launchPost-launch iteration velocity (planned)
Estimate: enabled by CI/CD + modular architecture. Replace with actual release logs.
2–4 releases/week
first 30 daysFirst-month activation target
Creative estimate for net-new product; replace with funnel analytics later (trial → activated org).
25–75 activated orgs
30 days post-launchWhat changed day to day
The part of the result that never shows up in a dashboard, but is the reason the numbers held.
Cosellr launched as a cohesive end-to-end platform rather than a set of disconnected AI demos.
The MVP supports real usage immediately: onboarding, targeting, outreach generation, nurturing, and meeting follow-through.
Teams can iterate quickly without destabilizing production because observability, CI/CD, and operational patterns were built in from day one.
A scalable foundation exists for additional channels, new agent workflows, and deeper sales-cycle automation.
Your turn
Run this play with your team.
We built Cosellr’s AI-first sales platform from scratch in 8 weeks—launching a production-ready MVP across prospecting, outreach, nurturing, and in-call agent assistance. If that shape looks familiar, the first conversation is a working session, not a pitch.
Rapid MVP Development (8-week build)
Full-Stack Product Engineering
AI Agent Productization
Platform Engineering & Cloud Architecture
Security & Access Controls
Observability & Production Readiness
What Cosellr got
Time to MVP (net-new build)
8 weeks
end-to-end delivery
Core workflow coverage
4/4 pillars shipped
within 8 weeks
Time-to-first-value target (new user)
≤ 30 minutes
post-launch target