Service
AI SaaS Development
We build software products with AI at their core, engineered for the economics that come with them: per-tenant inference cost, usage metering, evaluation pipelines and quality that holds as you scale.
What is AI SaaS development?
AI SaaS development is building a subscription software product whose core value comes from model inference rather than from stored data or workflow alone. It combines standard SaaS engineering — tenancy, billing, onboarding — with concerns specific to AI: variable per-request cost, non-deterministic output, evaluation instead of simple assertions, and pricing that must stay ahead of inference spend.
The distinguishing constraint is margin. In conventional SaaS the marginal cost of a request is close to zero. In AI SaaS every request has a measurable cost, so token accounting, caching, model routing and plan design are architectural concerns rather than finance concerns.
An AI SaaS product inherits every requirement of ordinary SaaS and adds several that do not appear elsewhere. Output is probabilistic, so quality is measured by evaluation sets rather than assertions. Cost is per-request, so unit economics must be visible per tenant. Providers change models and pricing, so the product must be portable.
We build with those constraints in the architecture. Inference goes through an internal gateway that handles routing, caching, retries and token accounting. Evaluation suites run in CI. Usage is metered at the same granularity it is billed at, so the invoice and the cost ledger reconcile.
Business problems
What this usually solves.
The situations clients describe when they start this conversation.
Margins that fall as usage grows
Flat-rate pricing meets variable inference cost and the heaviest customers become the least profitable.
Quality regressions nobody notices
A prompt or model change improves one case and silently degrades ten others because there is no evaluation baseline.
Provider lock-in
Calls to one vendor SDK are scattered through the codebase, so a price change or deprecation becomes a migration project.
Latency that breaks the interaction
Multi-step chains run sequentially with no streaming, so users wait through a blank screen and abandon the feature.
Capabilities
What we bring to it.
Inference gateway
A single internal boundary for model calls: routing by step, caching, retry and fallback, and per-tenant token accounting.
Metering and AI-aware billing
Credits, usage tiers and overage handling metered at request level and reconciled against provider spend.
Evaluation pipelines
Curated case sets with expected behaviour, scored automatically on every change and tracked over time.
Retrieval architecture
Per-tenant embeddings and indexes with strict isolation, so one customer’s documents can never surface in another’s results.
Streaming interfaces
Token streaming, partial rendering and optimistic UI so long operations feel responsive.
Safety and guardrails
Input validation, output schemas, content policy checks and refusal handling, applied consistently across features.
Deliverables
What we build
- AI-native products built from the ground up around model capability
- AI feature layers added to an existing SaaS platform
- Document intelligence products with extraction and validation pipelines
- Copilot surfaces embedded inside vertical software
- Per-tenant knowledge platforms with isolated retrieval
- Usage-metered AI APIs offered to your own customers
Stack
Technology approach
Every model call passes through one internal gateway. That single boundary is what makes routing, caching, cost attribution and provider portability tractable.
| Layer | What we use |
|---|---|
| Product | Next.js with streaming server components and optimistic updates |
| Gateway | Provider-agnostic inference layer with routing, caching and accounting |
| Retrieval | PostgreSQL with pgvector, per-tenant namespaces and hybrid ranking |
| Async work | Queue-backed workers for batch processing and long chains |
| Billing | Stripe with usage records driven from metered events |
| Quality | Evaluation suites in CI with scored regression tracking |
Process
How we deliver.
- 01
Discover
Understand business requirements and existing systems.
- 02
Architect
Design product, cloud, integration and data architecture.
- 03
Engineer
Build production-grade software.
- 04
Launch
Deploy, integrate and validate.
- 05
Scale
Optimize, monitor and evolve.
Case studies
Relevant work
FAQ
AI SaaS Development — questions we are asked
How should an AI SaaS product be priced?
Usually a hybrid: a platform fee that covers fixed cost plus a usage component tied to the metered unit your customers understand — documents processed, agent runs, seats with a fair-use ceiling. Pure flat-rate pricing on a variable-cost product transfers all the risk to you.
How do you test something non-deterministic?
With evaluation sets rather than exact-match assertions. Real inputs are paired with expected behaviour and scored on every change, so you see the direction quality moved rather than a pass or fail on a single case.
Can we switch model providers later?
If inference is behind a gateway, yes — it is a configuration change plus an evaluation run to confirm quality holds. If provider SDK calls are spread through the codebase, it is a rewrite. This is the main reason we insist on the gateway from the first feature.
How is tenant data kept separate in retrieval?
Embeddings are namespaced per tenant and the isolation is enforced at the query layer, alongside the same row-level security that protects relational data. Retrieval is never filtered only in application code.
Planning ai saas development work?
Tell us about the systems involved and the constraints. We will come back with an architecture and a delivery sequence.