Entering a new market in 10 weeks.
A leading APAC Corporate Services SaaS firm with over $200M in revenue needed to extend their platform into a new geography. The scope was broad, the deadline was fixed, and the deeply interconnected, multi-market platform was one of the most complex codebases we've seen.
Decades of business logic. Ten weeks to extend it into a new market. Eight realfast engineers.
Extend a decades-old platform into a new market. Internal estimate: 8 to 9 months.
The client — a leading APAC Corporate Services SaaS ($200M revenue) — needed to extend their platform into a new geography — a major expansion that touched payments, compliance, reporting, and multiple interconnected modules built over decades. The internal estimate for this work was 8-9 months.
The legacy platform itself was a challenging constraint. Five million lines of code, multi-currency, multi-market — where any change in one module carried downstream consequences across others. This was precision surgery on a live, complex system that serves customers across APAC.
The scope was broad, the deadline was fixed, and the platform could not break.
A fixed launch date meant the market-entry window could not slip. But the platform was live, interconnected, and unforgiving: a mistake in payments or compliance would ripple across every market it already served. Moving fast and breaking things was not an option; the work had to be both fast and exact.
We ran an AI-first delivery model: agents for speed, engineers for judgment.
The realfast approach to AI-first delivery emerges out of two areas of expertise: our mastery of frontier models and coding tools, and building human+AI hybrid teams that combine AI-driven speed with human-driven judgment.
In practice, this translates into three layers of execution: what we fully delegate to agents, how agents amplify our engineers, and where human judgment remains irreplaceable. Underpinning these are deep code intelligence across the full codebase, and continuous tooling research so the team is always working with the best available models for the job.
- Delegate. Templated documentation, weekly status reports from Jira, and discovery-call transcripts, handled by agents.
- Amplify. Agents navigated the codebase, drafted implementation plans, queried data models, and mapped dependencies alongside our engineers.
- Judge. No single agent catches everything at this scale, so work was cross-verified across 2 to 3 agents, with engineers owning every tradeoff.
A daily delivery cadence, running on agent-scale infrastructure.
Engagement setup
The moment we were onboarded, our infrastructure kicked in: agents indexing the client's codebase, mapping critical paths across their multi-market platform, tracing flows from UI to database. Skills and MCP integrations were configured for their specific toolchain - Jira, TestRail, Mantis, and the internal systems their teams use daily. By the time the first sprint started, our team had the scaffolding to navigate a codebase they'd never seen before.
The first technical analysis, covering a scope the client's internal teams had estimated at 1-3 months, was delivered in under a week.
Agent usage
From there, we structured agent usage across two layers:
- Documents structured as templates and populated in TestRail, Mantis, and the client's internal tools.
- Weekly status reports generated from the client's Jira data.
- Discovery call transcripts parsed, screenshots extracted from raw video so the team could reference the exact client conversation later.
- Navigating the client's codebase alongside their call recordings and scope documents, making informed tradeoffs without waiting for the next overlap window.
- Drafting implementation plans, querying the client's databases to understand their data models, and setting up exact test data matching their production schemas.
- Building custom code indexers to map dependencies across the platform, and custom UIs to turn agent-generated markdown into navigable architectural views.
At this scale, no single agent catches everything. We cross-verified work across 2-3 agents, each catching things the others missed.
Tooling agility
Claude Code was our primary tool from day one. As the engagement progressed, Codex emerged as a powerful option as well. Given the complexity of the codebase and the fixed deadline, we adopted both - leveraging the unique strengths of each tool to give our engineers broader coverage across five million lines of code, maintaining delivery velocity without any drop in quality.
AI tools and model capabilities are improving every day. Our true edge at realfast isn't a specific tool, it's the operational framework that turns these models into outcomes.
Delivery in motion
From the second week of the engagement, the client had deliverables moving through the system. Every 24 hours, the client's team had something to review: a PR ready for sign-off, a clarification on a scope decision, tickets moving into QA.
Because agents handled documentation for every PR and systematically traced every flow in the codebase, the traceability was better than most manual processes. Our engineers even uncovered an unrelated production API issue in the client's existing platform, which was flagged to their engineering leadership and resolved. When the client's QA team raised five issues during UAT, realfast diagnosed, fixed, and verified four of them within a single day.
Work the client scoped at 8 to 9 months, delivered in roughly 10 weeks.
Three things this engagement proved.
Context compounds.
The delivery cadence - new scopes absorbed mid-flight, zero tickets stuck in development by week 8 - came from the discovery work realfast did before the first ticket was written. At the end of week 1, we shared complete documentation of the client's system and processes to build common understanding. When they started building, they did so with context.
Agent-scale delivery requires robust infrastructure.
If not planned carefully, operating at this cadence means exponentially more commits, PRs, and deploys hitting the client's CI/CD pipeline every day, at volumes most pre-agentic engineering environments are not built for. We work with the clients' product and engineering teams to ensure the SDLC can operate at this speed.
The ratio inverts.
A traditional services team of eight engineers on this engagement would have spent most of their time writing code and escalating questions. realfast's engineers spent roughly 70% of their time on understanding and judgment, 30% on code. A daily delivery cadence makes this possible. This is an example of superhuman delivery made possible with AI.
Agent-scale delivery is a team sport, and this partnership set the pace.
Agent-scale delivery is a team sport. realfast can compress discovery, structure agentic workflows, and maintain a daily delivery cadence. But none of that matters if the client's organisation isn't playing by agentic rules. On this engagement, the client didn't just keep up. They set the pace.
This is rare. Most organisations say they want to move fast. This client's organisation actually moved fast because the results warranted it. When a client is fully committed to an AI-native delivery cadence, the results are genuinely transformative.
Experience the power of agents, firsthand.
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