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// Custom AI Development · SaaS

Custom AI development for SaaS that ships AI as a feature, not a bolt-on.

Every SaaS founder is being told to "add AI" right now. The default options are bad: a chatbot widget that does not understand your product, an OpenAI API call wrapped in a panic, or a six-month enterprise consulting engagement to design "your AI strategy". We build the third option: production AI features architected into your product, with your data, your users, your billing, and your retention metrics in mind.

Why most SaaS AI features fall apart in production

The pressure to ship an AI feature is everyone's problem right now. Boards ask about it, customers ask about it, your competitor just launched one, your investors put it in the last memo. The fast paths all have hidden costs. Bolt a chatbot widget on with a no-code tool: looks live for the demo, hallucinates pricing for paying customers in week two. Wrap an OpenAI API call in your existing code: works for one feature, breaks the moment the vendor changes pricing or a model deprecates. Hire an enterprise consulting firm: six months and a strategy deck, no production code.

The deeper problem is that AI features that work in production look nothing like the AI demos that win conference talks. Production AI needs vendor abstraction (so a model deprecation does not break a paying feature), prompt-and-tool engineering (not "let the model figure it out"), evaluation frameworks (because "looks fine when I tested it" is not a quality bar), monitoring (because models drift), and cost controls (because token bills compound silently). None of this lives in the marketing pitch for AI features. All of it has to be in the codebase.

For SaaS founders, the cost of getting this wrong is not just engineering time. It is feature credibility, support load when the AI hallucinates, churn from users who tried it and found it broken, and the strategic option of charging for AI features later if you cannot land the first one cleanly.

How we build it differently

We architect AI features into your product, not on top of it. The first session is a product-and-data review: what your users actually do, what data you already have, what feature would change retention or expansion if it worked, and how the AI feature integrates with your existing user model, data model, and billing. Most SaaS AI feature ideas survive contact with this review; some get reshaped or paused. Both outcomes save months.

Then we build a multi-provider abstraction layer. The feature can route to OpenAI, Anthropic, or open-weights models as appropriate, with the routing logic in your code and the secrets in your vault. When Anthropic ships a better model, you switch with a config change. When OpenAI changes pricing, you renegotiate with leverage. Vendor lock is a choice, not a default.

Tool use over pure prompting is non-negotiable for any feature that touches structured data. The AI does not "decide" facts about your users, it calls functions you control that return real data. The output is auditable, deterministic where it needs to be, and reproducible. Same discipline we use across every project we ship; the Mobilni Market case study walks through how this works for a B2B retail product, and the pattern transfers cleanly to SaaS.

Evaluation is built in from day one, not bolted on after launch. Before any AI feature ships to paying users it has a reference dataset, a set of failure-mode tests, and a regression suite that runs on every prompt or model change. Monitoring continues post-launch with explicit alerts for drift, cost spikes, and quality regression.

What we ship for a SaaS client

  • Architecture review: a written document mapping where AI fits in your product, what it improves, and what it does not
  • Multi-provider abstraction: model routing layer that lets you switch vendors without breaking features
  • Custom prompt + tool stack: tuned to your domain, your users, and your data
  • Evaluation framework: reference dataset, failure-mode tests, regression suite on every prompt or model change
  • Cost monitoring + controls: token-usage tracking, per-user limits, alerting on anomalies
  • Observability: structured logs of every AI call with input, output, and downstream effect for debugging and audit
  • Integration with your stack: your existing user model, data model, billing system, and authentication
  • Optional: in-product agent layer for workflows that need multi-step reasoning across user data

// FAQ

Frequently asked

How does this differ from just calling the OpenAI API ourselves?
For a prototype, it does not. For a production feature that paying users depend on, the difference is vendor flexibility (no lock-in to one model provider), prompt + tool engineering discipline (not "let the model figure it out"), evaluation framework (so quality regressions get caught before users hit them), and monitoring (so cost spikes and drift get caught before they hit your P&L). The first AI feature is straightforward. The seventh one is where the architecture pays off.
Are you going to lock us into a specific model provider?
The opposite. We build a multi-provider abstraction so OpenAI, Anthropic, Cohere, or open-weights models like Llama or Mistral can route to the same feature. Switching is a config change, not a refactor. Vendor flexibility is part of the build, not something you get to later when something breaks.
How do you evaluate whether the AI feature is actually working?
Before the feature ships we build a reference dataset of real-product inputs and the outputs you would call "correct" or "acceptable". Every prompt change, every model swap, every code change that touches the feature runs against this dataset. Quality regressions get caught in CI, not in customer-support tickets. The eval framework is part of the codebase, not a spreadsheet that gets out of date.
Time-to-first-production-feature?
A first focused AI feature ships in 4-8 weeks from kickoff to production, depending on integration depth. Full scoping and milestone breakdown lives on our process page. The first feature is always self-contained and shippable to a real user segment, even if a longer roadmap follows. The architecture (multi-provider abstraction, evaluation framework, monitoring) gets built alongside the first feature so the second one is faster.
Do we have to use a frontier model or can open-weights work?
Depends on the use case. Frontier models are usually the right call for features where output quality directly affects user satisfaction (anything user-visible, anything that touches billing or pricing). Open-weights are often the right call for high-volume internal features (data classification, enrichment, summarization) where cost and latency matter more than the last 5% of quality. The architecture supports both; we pick per feature, not per project.
How does this work with our existing engineering team?
Most engagements pair us with your engineers, not replace them. We bring AI-specific expertise (prompt engineering, evaluation, multi-provider patterns, production-AI failure modes). Your team brings the product, data, and infrastructure context. Documentation and handoff are explicit so the AI features remain owned by your team after we ship, not held hostage to a vendor relationship.

How we estimate the work

Pricing follows scope, not a fixed rate card. The full breakdown of how we scope, ship in milestones, and what each phase includes lives on the process page.

See our process →

Ready to talk about a saas project?

A discovery call is free and runs about 30 minutes. We map the problem, tell you if we are the right fit, and walk out with a first-milestone outline.

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