TL;DR Playbook
- Build an intelligence layer that completes workflows.
- Be AI-native in development and product.
- Choose boring where it compounds; set SLOs early.
- Hire for philosophical alignment, ownership, and discipline.
- Prove value fast with a clean PLG motion.
Run this, and you won’t just remove hours of busywork—you’ll give every team a lever that moves their business forward, reliably, on purpose, and at speed.
You’re four people, some scrappy code, and a line of teams begging to get their hours back. That’s the perfect moment to decide what kind of company you’ll become. Not in press releases—through the systems, habits, and architectural bets you make now. Below is the pragmatic, AI-native playbook I’d run to scale a workflow-time-slayer from post-MVP to durable, venture-ready growth (without turning the product into a Rube Goldberg machine). (Quick note: I’m keeping this fast, punchy, and actionable—because speed matters.)
1) Ship outcomes, not demos.
If your promise is “we reduce the time teams spend on workflows,” then your north star isn’t “generate content,” it’s “complete the job.” Build for end-to-end workflow completion: ingest the right context, decide, produce the artifact, and hand it off with zero extra steps. That means a context-aware engine, not a prompt party—an intelligence layer that learns from feedback, orchestrates multiple models, and degrades gracefully via fallbacks when an LLM gets cute. Users care that the work is done and ready, not that it’s “AI.”
2) Design the intelligence layer first.
Your core moat is how you assemble context: CRM facts, product telemetry, documents, and relevant market data—unified, ranked, and routed to the right model at the right moment. Prioritize a multi-model orchestration layer with explicit policies for routing, tool-use, and fallbacks. Bake in observability at the token and tool level. Yes, that’s heavier than sprinkling prompts, but it’s the only way you get reliable, outputs at scale. Build it once, reuse everywhere.
3) Be AI-native in development, not just in product.
Every engineer should pair with an AI code assistant; treat AI tooling as mandatory PPE for software work. Create guardrails (linting, tests, CI) so AI-accelerated velocity doesn’t become AI-accelerated entropy. Track PR cycle time, lead time for change, and escaped defects weekly. The goal: ship more, ship safer. Make the culture explicit: we use AI to move faster without lowering the bar.
4) Pragmatism beats purity—choose boring where it counts.
Pick a well-trod web stack, lean into managed cloud, and invest in build/deploy automation before you hire the next engineer. For the core service, design for horizontal scale and graceful degradation; for the UI, optimize for fast iteration and mobile-clean layouts. Save fancy for where it differentiates (your intelligence layer). Pragmatism also means SLOs: target 99.9% availability and keep generation times tight. You can be opinionated and realistic.
5) Own reliability like it’s a feature.
Treat reliability and correctness as part of the product experience, not “infra.” Define “done” to include deterministic templates, idempotent generation, and human-in-the-loop review where the stakes are high. Add shadow-runs, golden-set tests for prompts, and model-version pins. Make failure modes boring and recoveries automatic. This is how you confidently scale to thousands of outputs per month without waking people up at 3 a.m.
6) Integrations are a strategy, not a checklist.
Your product’s superpower is context. To earn it, you need first-class connectors into the systems customers already live in—CRM, usage analytics, document stores—plus a clean data fabric that normalizes and enriches what you ingest. Don’t chase “150 integrations” as a vanity number; chase the five that unlock 80% of your use cases, then expand. Architect the integration layer so adding source #101 is connective tissue, not open-heart surgery.
7) Culture: clarity, context, customer.
Scaling is a culture problem dressed as a throughput problem. Rally the team around ruthless clarity (what exactly we’re building and why), a bias for action (ship, learn, repeat), and deep customer empathy (talk to users weekly, watch recordings, read support threads). And sweat the details—from template polish to naming conventions—because customers feel the difference between “generated” and “crafted.” Make transparency default: say the quiet part out loud internally, own tradeoffs externally.
8) Ownership with discipline.
Give problems, not tasks. Each engineer owns a surface (e.g., “document creation quality” or “data freshness”), with clear metrics and on-call rotation. Codify discipline through lightweight RFCs, definition of ready/done, and a weekly demo ritual. Move fast, yes—but with repeatable checklists, crisp incident reviews, and a habit of measuring before optimizing. High standards, low ego, fast recovery.
9) Philosophical alignment matters more than headcount.
With a few people, one misaligned hire is 20% cultural drift. Hire for people who love outcomes over optics, who are customer-obsessed, and who can balance technical excellence with business pragmatism. Bonus: excellent candidates have the ability to bring two or three exceptional peers with them. That accelerates the next phase—building the first great team, not just the first bigger team.
10) Show traction the market actually believes.
Investors and enterprise buyers both respect the same proof: consistent usage growth, time-to-value measured in minutes (not weeks), and expansion signals from early adopters. If you’re post-MVP with a live pipeline, translate that into a crisp plan: which milestones unlock the next stage of scale (e.g., reliability SLOs, key integrations, and a repeatable onboarding motion). Keep the goal line in sight: “ready-to-go outputs” that close loops for teams drowning in busywork.
11) Product-led growth with a grown-up backend.
Offer a “try before you buy” path where users can create real outputs, with usage-gated paywalls and clean limits. Remove friction on day one. Instrument the funnel: time-to-first-value, task completion rate, edit distance to “final,” and the number of times an artifact is shared with a real stakeholder. PLG isn’t “free forever”; it’s prove value fast, then scale carefully.
12) Roadmap like a portfolio manager.
Split the next 12–18 months into a few durable tracks: Reliability & Speed (SLOs, caching, fallbacks), Data & Integrations (the top 5 sources by impact), Creation Quality (templates, review tools, learning loops), and Enterprise-Readiness (audit logs, permissions, SLAs). Each track gets outcomes, not outputs. Review weekly, reset monthly. Your scoreboard: thousands of high-quality artifacts generated on schedule, 99.9% uptime, and delighted users who invite their boss to the next demo because it saves them hours.
13) Fundraising posture: prove the engine, then pour fuel.
If you’re raising, lead with operating proof: speed to value, integration attach rate, and the conversion from “first artifact” to “team rollout.” Tie capital to specific capacity expansions (e.g., additional connectors, enterprise security features, or model-ops hardening), not generic burn. Investors don’t fund potential energy; they fund systems that already move.
14) The non-negotiable promise.
You exist to give people their time back. That means clarity over chaos, context over noise, action over theater. Keep the product honest: if users see it, care about it; if it breaks, learn from it; if it helps, double down. Do the clever thing when it simplifies the hard thing—and default to transparency when it doesn’t. That’s how a tiny team builds a category-defining company that lasts.