When AI is the product layer, versioning the model, evaluating its behavior, and testing its outputs are engineering responsibilities, not afterthoughts. We build the infrastructure that makes those responsibilities tractable: evaluation pipelines, prompt versioning, and behavior tests that ship alongside the feature, not after the first quality complaint. As a software development company serving San Francisco's AI-native startups and enterprise platforms, we bring that engineering discipline to every AI product we ship.
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You are probably here because one of these is true.
What you get from a software build.
AI-native products and enterprise platforms in San Francisco rarely need every pillar at once. Most builds focus on two or three, and all six are part of the same software development work.
A data model that captures model behavior over time, settled before features.
Domain modelingAPI contract designTypeScript & GoEval-ready data model
A platform where model outputs shape the user experience needs its data model built to capture behavior over time: what the model was asked, what it returned, what the user did next, and whether the response was rated or flagged. Settling that schema before any feature is built is what makes the evaluation infrastructure useful rather than retrofitted.
Enterprise-ready isolation, SSO, and billing that scale with model usage.
Multi-tenant architectureUsage-based billingRole-based accessAudit logging
A platform selling to enterprise buyers in San Francisco's SaaS market needs tenant isolation, SSO, and audit logging ready before the first large account's IT team asks for them. That also means usage-based billing that scales with the model's token consumption, not flat subscription pricing built before AI cost structures existed.
Native or cross-platform, decided by what the AI feature needs in hand.
Native iOS & AndroidFlutter & React NativeReal-time AI featuresApp Store & Play Store release
A mobile product with AI features built in at the interface level needs a native build when the interaction model requires low latency, on-device processing, or camera and microphone access a hybrid layer adds friction to. A B2B companion app or internal tool ships fine cross-platform — it depends on what the AI feature needs to do in the user's hands.
The evaluation dataset ships alongside the feature, not after the complaint.
LLM & RAG pipelinesEvaluation frameworksGuardrails & fine-tuningDocumented model decisions
AI development for a product where the model is the core feature means shipping the evaluation dataset alongside the feature, not after the first quality complaint. Model behavior tests and a documented decision trail between model version and expected output keep the product maintainable rather than fragile.
The specific components blocking clean AI integration, isolated and replaced.
Strangler-pattern migrationZero-downtime cutoverFramework & database upgradesPhased rollout
A San Francisco enterprise or scale-up built five years ago typically has one or two components blocking modern AI features: a synchronous API that can't stream, a data model unusable as training data, or an authentication system too brittle to extend. Isolating and replacing those directly makes AI integration possible without a full rewrite.
Infrastructure as code and CI/CD covering application and model behavior.
Infrastructure as codeCI/CD for model deploymentsMulti-cloud setupsObservability
Vibe coding cleanup for a San Francisco startup often starts here: the application logic works but the infrastructure is held together by manual steps, undocumented environment variables, and a deploy process that only the founder understands. Getting infrastructure as code, a proper CI/CD pipeline, and monitoring that covers both application and model behavior is the foundation that makes a team confident to ship.
Built for every vertical.
Why teams pick us for this.
Whether an AI feature holds up is decided by edge-case inputs at scale, not the demo — so evaluation is a first-class part of the architecture.
An engineer who's shipped model-as-deliverable products before knows exactly where the fragility lives — in prompt dependencies and latency assumptions.
When a constrained, tested integration serves the product better than a flexible one, that's the call, even if it limits certain use cases.
The engineer who designed the evaluation framework also implements the features that depend on it. Nothing gets reconstructed in between.
Our achievements display our capabilities
Selected work real outcomes.
AI-native platforms, enterprise SaaS products, and developer tools built for San Francisco's technology market sit alongside API-first infrastructure and post-vibe-code production systems.
View All Case StudiesSenior from the first conversation, not just the proposal. Our software developers in San Francisco stay on through release, whichever pillar you start with, not a rotating cast handed off between phases.
Start a conversation{ 01 }· Week 1
We start by understanding what the model is responsible for, what correct behavior looks like, and what the architecture needs to guarantee, then settle the data model before design begins.
{ 02 }· Week 1 to 2
Design and API architecture run in parallel, so the interface and the backend line up from day one.
{ 03 }· Ongoing sprints
Build happens in two-week sprints, each producing a working version tested against real data, not a single drop at the finish line.
{ 04 }· Launch and after
We manage deployment and rollout, then remain on the project for monitoring, fixes, and new features once it is live.
Trusted voices. Real outcomes.
“We truly appreciated their dedication, technical expertise, and problem-solving approach.”
Young Onion
Department Head
“I was blown away by the knowledge the team had about creatives, e-commerce, website design, and optimization.”
Decathlon Sports India
Image Leader
“They have a good team of designers and project managers who help us with the designs using HTML, Angular, and React.”
Instarama
COO
“Their creativity stands out. A collaborative team that delivered high-quality solutions working closely with us.”
CodeGama LLP
Business Developer
“The product has become more intuitive and user-friendly. Load times dropped significantly after their work.”
Qoruz
Co-Founder
“Their commitment to timely delivery was impressive.”
CurleyStreet Media
Business Development Rep
“We truly appreciated their dedication, technical expertise, and problem-solving approach.”
Young Onion
Department Head
“I was blown away by the knowledge the team had about creatives, e-commerce, website design, and optimization.”
Decathlon Sports India
Image Leader
“They have a good team of designers and project managers who help us with the designs using HTML, Angular, and React.”
Instarama
COO
“Their creativity stands out. A collaborative team that delivered high-quality solutions working closely with us.”
CodeGama LLP
Business Developer
“The product has become more intuitive and user-friendly. Load times dropped significantly after their work.”
Qoruz
Co-Founder
“Their commitment to timely delivery was impressive.”
CurleyStreet Media
Business Development Rep
“Simply put, the quality of their code is excellent. They integrated third-party software and ensured GDPR compliance.”
VIA IOM
Director
“What impressed us most was how well they understood our brand and translated it into clean, thoughtful designs.”
GD Farm Fresh
Director
“Their team was patient, courteous, responsive, and technically proficient throughout the entire project.”
Studio by Nandita Manwani
Partner
“Zethic Technologies generally delivers on time and in line with our requirements – deployed in 30+ countries.”
SABA Hospitality
Executive Director
“Zethic built the features specifically to match our internal workflow and business needs. Professional and on time.”
Coral Publishers
Executive
“Zethic Technologies is a true partner.”
Geordana
CEO
“Simply put, the quality of their code is excellent. They integrated third-party software and ensured GDPR compliance.”
VIA IOM
Director
“What impressed us most was how well they understood our brand and translated it into clean, thoughtful designs.”
GD Farm Fresh
Director
“Their team was patient, courteous, responsive, and technically proficient throughout the entire project.”
Studio by Nandita Manwani
Partner
“Zethic Technologies generally delivers on time and in line with our requirements – deployed in 30+ countries.”
SABA Hospitality
Executive Director
“Zethic built the features specifically to match our internal workflow and business needs. Professional and on time.”
Coral Publishers
Executive
“Zethic Technologies is a true partner.”
Geordana
CEO
Ways to work with us.
One team, one way of working, regardless of whether you are building something new or extending what is already live. Most clients pick a starting point and move to the other option later, once the project's shape becomes clearer.
A system scoped with a fixed feature list, timeline, and price tag. Works best when you already know what the first version has to include.
A senior pod that plugs into your existing tools and sprint cadence, shipping on an ongoing basis. Suits teams that need more capacity without running a full internal hiring process.
Questions, answered.
San Francisco has the highest software engineering rates in the US, driven by AI talent concentration and competition from hyperscalers. The scope of the AI architecture and the evaluation infrastructure required shapes the number more than the feature count. Share what the product needs to guarantee, and we'll come back with a real number.
The evaluation dataset, the prompt versioning strategy, and the data model for capturing user interactions are the decisions that determine whether the product stays maintainable as models improve. Getting those right before the first feature is built is what separates a product that can ship confidently from one that can't.
Every third-party dependency is a boundary the product doesn't control. We map those boundaries explicitly during architecture planning, design graceful degradation for each one, and test failure modes before launch rather than after. The integration contract matters as much as the feature it enables in fintech APIs.
Fixed-scope suits a defined first version where price and delivery date, locked to a feature list, are the right structure. A dedicated team suits an evolving product where fintech startup risks and new model capabilities keep changing the roadmap, and a monthly-billed senior pod handles that better than a locked quote can.
An AI-native v1 with evaluation infrastructure takes longer than a standard SaaS product, since the eval framework is a deliverable alongside the features. Once we understand the model's role and what correct behavior looks like, we can put a specific timeline on it.
AI-native products with multi-user features hit the same consistency problems marketplaces do, which is the strength of our Seattle team. One playbook, run by the software development company for the US, powers AI software development in San Francisco too.
Tell us what the model is responsible for and what the product needs to guarantee. A senior engineer replies within one working day.
Tell us what you have
The product, the model's role in it, and where the architecture stands today. We sign an NDA first.
Speak to an engineer
Someone who has shipped AI-native products before, not an account manager relaying the question.
Get an architecture read
What the evaluation infrastructure needs, what can be kept, and a timeline with an engagement model that fits.
Tell us what the model is responsible for and what the product needs to guarantee. A senior engineer replies within one working day.
