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Portland, OR: Series B Engineering Teams
You have a working product, a paying customer base, and a roadmap. You do not need a rebuild. You need AI features that ship without blowing up your team or your AWS bill.
We embed specific AI capabilities into your existing SaaS: smart search that understands intent, auto-categorization that replaces manual tagging, and draft generation that fills empty states with useful output. Each feature ships behind a flag, with cost controls built in from the start.
Talk to an engineer, not a salesperson
Describe the AI feature you want to add. We'll tell you honestly whether we can build it, what it will cost, and how long it will take.
Four engineering practices that separate AI features that work in production from ones that work in a notebook.
Every AI feature ships behind a flag. You control which tenants see it, at what percentage, and for how long. No big-bang launches that you cannot pull back.
We instrument every inference call with per-tenant cost tracking, set budget limits by plan tier, and add graceful throttling before you hit a surprise invoice. Series B economics require this from week one.
Prompts are versioned artifacts stored in your database, not strings buried in code. Roll back a bad prompt in seconds. Track which prompt version produced which output for any user session.
AI features that make users wait 8 seconds for a response feel broken. We implement streaming responses with progressive rendering so the product feels fast even when inference is slow.
Portland has a dense cluster of product-led SaaS companies at Series B and C stages. Vacasa engineering teams are adding AI-powered property search that understands traveler intent beyond keyword matching. Puppet and Perforce developer tool teams are embedding config intelligence that surfaces anomalies before they reach production. These are not greenfield AI products; they are specific features added to products that already have customers.
At Series B, the AI ROI story is retention and expansion, not acquisition. A feature that reduces churn by 6 points or increases seat expansion by 15% makes the board meeting. We help Portland engineering teams identify which AI features move those metrics and build the ones worth building.
Portland engineering culture favors lean team ownership. We design every AI feature so a single mid-level engineer can operate it after handoff. No dependency on us to keep the lights on.
Embedding intent-aware search into property management SaaS so "cabin near a lake that allows dogs" returns correct results without manual attribute tagging.
Adding AI-powered drift detection to developer tool SaaS so configuration anomalies surface in the UI before they cause incidents downstream.
Auto-classifying product catalog entries and generating attribute tags so merchandising teams stop spending half their sprint on manual data entry.
Scoped and priced based on feature complexity and data pipeline work, agreed before any code is written.
A written spec for the AI feature: inputs, outputs, failure modes, and the user-facing UX. Agreed before any code is written.
The AI feature integrated into your existing codebase, with tests, behind a feature flag, ready for gradual rollout.
Per-tenant LLM cost tracking, budget limits by plan tier, and throttling logic. You see the bill before it arrives.
A versioned prompt store with rollback capability. Change prompts without a deploy. Know which prompt version affected which users.
Experiment setup so you can measure whether the AI feature actually improves the metrics you care about before full rollout.
Runbooks for operating the feature, onboarding docs for your team, and a guide for tuning prompts after we leave.
We build AI features behind feature flags so you control rollout pace. New AI surfaces deploy to 5% of users first, with A/B metrics running in parallel. If a feature regresses retention or engagement, you disable it with a single config change. Nothing touches your core product paths until you decide it does.
We instrument every AI call with per-tenant cost tracking from day one. You set monthly spend budgets per plan tier, and the system throttles gracefully when limits approach rather than failing hard. Most Series B SaaS products land between $0.40 and $1.20 per active user per month after we optimize prompt length and caching.
Yes. Prompts are versioned artifacts, not hardcoded strings. We store prompt templates in your database or config system with version IDs, so you can roll back a bad prompt the same way you roll back a bad deploy. Production incidents caused by prompt drift are a real problem; we solve it before it bites you.
For a well-scoped first feature (smart search, document summarization, or field auto-fill), we ship to production in 6 to 10 weeks. That includes integration, feature flag setup, cost instrumentation, and a streaming UX that does not make your product feel slow. The timeline extends if your data pipeline needs work before AI can access it.