01: The problem
You don't need an AI strategy. You need AI features that ship.
You've already built the product. Users are paying for it. Now your roadmap has an "AI" column, your investors keep asking about it, and your competitors are shipping features faster than your team can evaluate them.
Or your ops team is drowning in manual work. The same support tickets, the same data entry, the same internal reports built by hand every week. AI could replace half of it, but no one on your team has time to figure out how.
Most agencies will turn either of those into a 3-month rewrite. We won't. We add AI to what you already have, feature-flagged, behind a kill-switch, deployed to your accounts. If something breaks, we roll back. If it works, you keep shipping.
02: Right fit?
Is this the right fit?
Best for
- ✓SaaS companies with a live product and paying users, adding their first (or next) AI feature
- ✓Ops, support, finance, and HR teams looking to automate manual workflows with AI
- ✓Product leaders who want AI shipped, not piloted forever
- ✓Teams who want feature-flagged, non-destructive integration, not a rewrite
Probably not
- —Pre-launch products with no live users (start with our SaaS MVP service instead)
- —"We want to explore AI" with no concrete use case or rollout plan
- —Codebases so legacy that the integration is actually a rewrite (we'll tell you honestly on the call)
- —Single-use experiments with no production commitment
03: What you get
What you get when we ship
Every integration we ship is feature-flagged, observable, and reversible. Not a science experiment. Not a hidden side-project. A production AI feature your users hit on day one.
01AI features in your product
- Smart assistants (customer-facing chat, employee-facing copilots)
- AI-powered search across your data, docs, or knowledge base
- Auto-summarization, auto-categorization, auto-tagging
- Predictions, recommendations, scoring
- Personalization (different content, ranking, or workflows per user)
- Anything else where an LLM or ML model replaces a human decision
02Workflow automation
- Replacing manual ops work (data entry, ticket triage, report generation)
- Connecting your tools end-to-end (CRM ↔ email ↔ docs ↔ analytics)
- Custom AI agents that run on a schedule or trigger
- n8n, Zapier, Make pipelines, or fully custom when those don't fit
- Internal workflow agents for finance, HR, support, sales ops
03Integration safety
- Feature flags on every AI feature (instant rollback if something breaks)
- Evals that run before deploy and on every change
- Cost controls and usage limits per tenant
- Permissions-aware so users only see their own data
- Side-by-side testing, old behavior and new behavior run in parallel until you say go
04Observability & cost control
- Latency, accuracy, cost, and drift metrics from day one
- Per-feature dashboards (which AI features are used, by whom, at what cost)
- Alerts when costs or error rates spike
- Logs you can audit when something goes wrong
04: How we work
The Production Path
Every engagement runs on the same 4-phase delivery system. No surprises. No "discovery phase that never ends." No hourly invoices.
Phase 1
Discover
1 week
A paid Scoping Sprint. We map the integration points, audit your existing stack, define the AI feature scope, and deliver a fixed quote for the build. Ends with a go/no-go.
Phase 2
Build
3–8 weeks
We ship in weekly milestones. The integration goes in feature-flagged from the first commit. You see real working software every Friday, running against your real data, in a staging environment.
Phase 3
Harden
1 week
Evals, rollout plan, cost controls wired up, monitoring in place. We move the feature from staging to production behind a flag, then progressively roll it out to users.
Phase 4
Operate
30 days included, retainer optional
Post-launch monitoring included for 30 days. After that, optional retainer for monitoring, iteration, evals, and new feature work, most clients move into one once the first integration is live.
05: Stack
The stack we ship on
We pick the boring, battle-tested stack for everything around the AI, because the AI is novel enough; the rest of the integration shouldn't be.
- Models
- Claude, GPT-4o, Gemini, open-source (Llama, Mistral), selected per use case, latency, and cost
- AI tooling
- LangChain, LangGraph, LlamaIndex, Vercel AI SDK; custom predictive ML when an LLM isn't the right tool
- Retrieval & search
- pgvector, Pinecone, Weaviate, Qdrant Hybrid search (semantic + keyword), re-ranking, citation grounding Permissions-aware retrieval, users see only what they're allowed to
- Workflow automation
- n8n, Zapier, Make for standard pipelines Custom Python / Node workflow agents when off-the-shelf doesn't cut it Event-driven, queue-based, scheduled, or triggered patterns
- Integration patterns
- REST, GraphQL, webhooks, message queues SDK / API client work in your existing language (Node, Python, PHP, Ruby, Go) Database adapters for Postgres, MySQL, MongoDB, and friends
- Evals & observability
- LangSmith, Langfuse, RAGAS for AI evals Sentry, Datadog, OpenTelemetry for runtime monitoring Custom dashboards in Grafana, Metabase, or your existing tooling
- Feature flagging & rollout
- LaunchDarkly, Unleash, GrowthBook, or your in-house flags A/B test infrastructure for AI features Progressive rollout patterns (canary, shadow, ring deployments)
06: Why us
Why we'll still be here when you launch
The Production Guarantee
If your integration doesn't ship, we don't invoice the final milestone. We're accountable for production, not hours.
You own everything
Code, prompts, model choices, infrastructure. Hosted on your accounts. No license fees. No vendor lock-in. You can fire us and take the whole integration tomorrow.
Methodology in the open
The full Production Path is published. Phase by phase, what you get, what we need from you, exit criteria. The only thing we don't publish is pricing, that's quoted after scoping.
NDA standard
Every engagement is covered by an NDA by default. Mutual NDA on request before the scoping call.
07: FAQ
Frequently asked
How long does a typical AI integration take?+
3–8 weeks for the build, plus a 1-week Scoping Sprint up front. Total: 4–9 weeks from kickoff to live users on the new feature. Workflow automations are usually faster (2–4 weeks). The exact timeline lands in the fixed quote after scoping.
Will you touch our existing codebase?+
As little as possible. We integrate alongside your code, not into it. New AI features ship behind feature flags in clearly-bounded modules. If your stack genuinely needs surgery to support the AI feature, we'll tell you on the scoping call before you commit.
What if the AI feature breaks our app?+
That's why every integration is feature-flagged from the first commit. If anything misbehaves, we kill the flag, your existing app is untouched. Side-by-side testing runs the old and new paths in parallel until you're confident.
How do you handle AI costs?+
Cost controls on day one, per-tenant budgets, alerts on spikes, monitoring dashboards. We size the model to the use case, not the other way around. If a smaller model fits, we use it.
Can we own the prompts and the evals?+
Yes. Full source, full prompts, full eval suite, in your repo, on your accounts. We don't keep "secret sauce" hidden in our infrastructure.
Can you work with our existing development team?+
Yes, frequently. Many of our integration engagements are joint with the client's in-house team. We document properly, write code that reads like yours, and hand off cleanly.
What's the difference between AI integration and AI automation?+
*Integration* is adding AI **inside your product**, features your users see and use (chat, search, predictions). *Automation* is replacing **manual workflows** with AI, things your team does today that an agent could do instead. Many engagements include both.
Can you migrate us off an existing AI vendor?+
Yes. We've moved teams off vendor-locked AI features (proprietary chatbot platforms, locked-in vector stores) onto open infrastructure they own. Worth scoping if you're paying recurring fees for AI you'd rather control.