Industries SaaS & Enterprise Software
Developing the SaaSplatforms of tomorrow.
AI Engineering and software development for SaaS companies.
Getting an AI feature to demo well stopped being the hard part. Keeping it accurate across tenants who all have different data, isolated well enough for enterprise procurement, and cheap enough not to eat the gross margin is the work. We build that layer inside platforms that already have customers.
Autodesk Flow StudioAI VFX Production Platform
An AI production platform that turns a prompt into a rigged 3D character, drops it into live-action footage, and carries the shot through 3D scene editing and AI post-production. Studio-grade output, without a studio pipeline.
- 2.5×
- Faster processing
- 80%
- Automated VFX work
- 3 years
- Ongoing partnership
Why SaaS companiestrust us with delivery.
Most of this work is not greenfield. It happens inside a product that already has customers, an architecture someone else chose, and a release calendar that does not pause. This is why we staff for engineers who can read a system before they change it.
The codebase is already there
SaaS work rarely starts on an empty repo. It starts inside a live product with paying customers, an architecture someone chose years ago, and reasons behind the odd parts. Reading that comes before changing it.
Multi-tenancy is the first constraint
Every query, cache, background job, export, and log line has to know whose data it is holding. Isolation that survives the happy path and leaks on one edge case is what costs an account.
Every release lands on everyone
A deploy, a schema change, or a new model version reaches all your customers at once. Reversible migrations, regression suites, and flags that switch a change back off are what make releases routine.
Enterprise buyers arrive with a checklist
Moving upmarket turns SSO, granular roles, audit trails, data residency, retention, and subprocessor lists into product requirements. They come from buyers who will walk, and they cost far more to retrofit than to build in.
Ramp-up is the real delivery risk
Extra engineers only move a mature product once they know its domain, its conventions, and its release process. Time to a first meaningful pull request tells you more about a team than its size does.
What SaaS demandsfrom engineering and AI.
01Retrieval that respects permissions
Copilots and search over customer data have to honour the same row and role permissions the rest of your product enforces, at retrieval time rather than by filtering afterwards. Getting this wrong produces a feature that leaks quietly and convincingly.
02Agents with a defined blast radius
Autonomy is useful in proportion to how tightly its actions are bounded: which tools, whose data, what it may write, and where a human confirms. Simulation before execution and a full action log are what make an agent shippable to accounts that audit you.
03Latency that feels native
Users judge an AI feature against the rest of your interface, not against other models, and a ten-second spinner reads as broken. Meeting that bar takes streaming, aggressive caching, and the smallest model that answers each path well.
04Stability beyond the model provider
Provider outages and model deprecations arrive on someone else's schedule, while your SLA and release calendar stay yours. Fallback routing, pinned versions with tested upgrades, and a planned degraded mode keep the feature standing when the API behind it is not.
Real-life stories of triumph.
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I’m really impressed with their use of AI and the principles they’ve showed us. Nortik flags risks early and acts like a true partner and not just a vendor.
Nortik’s Impact
An AI workspace and studio that turns a brand’s own catalog into on-model imagery and virtual try-ons, cutting CO₂ from sample production by up to 75%.
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Recent Posts
Why AI prototypes fail in production (and how to ship one that doesn't)
Most AI prototypes stall somewhere between the demo and the deploy. The problem is rarely the model. It's the evals, guardrails, and infrastructure nobody scoped.
The eval suite is the product: testing LLM features like an engineer
Prompt changes without a test harness are blind bets. How we build eval suites from real traffic, and why they outlive every model you'll ever swap in.
What an AI agent actually costs to run in production
Token bills are the visible line item. The real costs hide in the architecture: retries, tool calls, human review loops. A breakdown from live systems.
Frequently asked questions
Everything product teams usually want to know before putting AI in front of their customers. Not here? Ask us directly.
Retrieval and good context engineering solve the large majority of product use cases, and they keep working when the base models improve. Fine-tuning earns its place for format adherence, a narrow domain vocabulary, or cost reduction on a high-volume path.
We'll usually recommend proving the feature with retrieval first. Fine-tuning a model around a product that's still changing is an expensive way to freeze a decision.
Tenant identity is carried through every layer: retrieval filters, cache keys, evaluation data, and logs. It is never applied as a filter at the end, and we test for cross-tenant leakage as an explicit case in CI.
Where a customer requires it contractually, we go further to dedicated indexes or dedicated deployments. That is a cost and complexity trade-off worth making deliberately rather than for every account.
Usually, and often substantially. The wins come from caching repeated context, routing easy requests to smaller models, trimming what gets sent on every call, and putting hard budget limits at the tenant level so one account can't consume a month's inference budget.
The prerequisite is per-feature, per-tenant cost telemetry. Most teams asking this question can't yet see where the money goes, and that's the first thing we build.
A golden set drawn from real usage across your tenant profiles, automated scoring calibrated against human judgement on a sample, and a regression gate in CI so a prompt or model change can't silently degrade an answer path.
Then online: feedback capture, sampled review, and per-feature quality tracking, because the offline set never covers what production sends you.
The monitoring half of this is familiar ground too. We built out the engineering team behind Observatory, the application WhyLabs customers watched drift, data quality and model performance from across thousands of features.
Yes. Subprocessor disclosure, data flow diagrams, retention and residency, opt-out of training, and the transparency obligations that increasingly come attached to AI features.
Your security and legal teams own the answers. We make sure the architecture supports the ones you want to be able to give, which is much cheaper to arrange before a deal is waiting on it.
You do. Code, trained models, pipelines, and documentation are yours, along with the infrastructure access to run them without us.
We hand over a codebase your own team can pick up. Nothing we build is designed to keep you dependent on us.