[ AI-Native App Development ]
Products Where AI Is the Core, Not a Bolt-On
MVP in 3 to 6 weeks, measured by evals from day oneWe design and build apps whose main workflow only exists because of a model: interfaces that generate themselves, agents that act as product features, and pricing that tracks real usage. Quality is measured by evals on every release, not by gut feel.
- MVP build after a one-week quality spike
- 3 to 6 weeks
- MVP build after a one-week quality spike
- Faster delivery with AI engineering
- 5x
- Faster delivery with AI engineering
- Select Partner
- OpenAI
- Select Partner
- Code and IP ownership
- 100%
- Code and IP ownership
AI-Native Products We Help Founders Build
An AI-native product has a core job that would not work without a model. These are the patterns we see most often.
Generative work tools
Apps that produce a first draft of a proposal, design, report or campaign from a short brief, with editing tools built around the model output.
Users start from a strong draft, not a blank page
Agent-as-a-product
Products where the main feature is an agent that does a job for the user, such as prospecting, research or bookkeeping, like the AI broker inside DomainMarkt.
Customers pay for outcomes, not seats
AI companions and coaches
Health, wellness or learning apps where a personal AI guide remembers context and adapts plans over time, similar to Healthzy and the Nutropx coach.
Higher engagement from personalized guidance
Vertical AI for regulated work
Domain products for compliance, legal or finance teams where answers must be cited, reviewable and consistent, as in the Sannos AI compliance engine.
Expert teams trust and adopt the output
Adaptive learning products
Courses and practice tools that generate questions, explain mistakes and adjust difficulty for each learner in real time.
Content that fits every learner without manual authoring
Insight and analytics products
Apps that read messy business data, find what changed and explain it in plain language with charts generated on demand.
Non-analysts get answers without a dashboard project
What We Build Into an AI-Native Product
AI-native products need product thinking, model engineering and unit economics designed together. These are the pieces we handle.
AI product strategy
Find the job only AI can do well, define what good output looks like and decide where humans stay in control.
Generative UI
Interfaces that render components, forms and charts from model output, with schemas that keep layouts valid and on brand.
Agents as features
Background agents that users can assign work to, watch progress on and approve, packaged as clear product features.
Usage-based pricing
Metering of tokens, tasks or credits per workspace, with plan limits, overage billing and margin tracking through Stripe.
Eval-driven development
Every feature ships with an eval set, and releases are gated on pass rate, latency and cost per task.
Streaming and feedback UX
Token streaming, partial results, regenerate and edit flows, plus thumbs and corrections that feed back into evals.
Model abstraction
A provider-agnostic layer so you can move between models as prices and quality change without rewriting features.
Product analytics for AI
Track acceptance rate, edit distance, retries and cost per active user next to your usual activation and retention metrics.
The Shape of an AI-Native Stack
The model sits in the middle of the product, so metering, evals and fallbacks are first-class parts of the architecture.
Product surface
What users see and shape.
- Generative UI
- Streaming editor
- Agent task inbox
- Feedback controls
Application core
Business logic, accounts and billing.
- Next.js or React app
- Workspaces and roles
- Usage metering
- Stripe billing
AI services
Prompts, agents and retrieval behind clean APIs.
- Prompt registry
- Agent runtime
- RAG index
- Model router
Quality loop
Decides what is good enough to ship.
- Eval suites
- Release gates
- Feature flags
- A/B experiments
Data and infrastructure
Where state, logs and models run.
- Postgres and pgvector
- Traces
- Cloud
- GPU hosting
The model sits in the middle of the product, so metering, evals and fallbacks are first-class parts of it.
From Concept to an AI-Native Launch
We test the risky assumption, usually output quality or cost, before building the full product around it.
1 week
Quality spike
Prove the core AI output on real examples, build the first eval set and model the cost per task against your pricing.
- Eval baseline
- Unit economics model
- Fixed quote
1 to 2 weeks
Clickable prototype
A working prototype with the real model behind it, put in front of target users to test the workflow.
- Live prototype
- User feedback notes
3 to 6 weeks
MVP build
Accounts, billing, metering, generative UI and release gates, built in two-week sprints with weekly demos.
- Launchable MVP
- Eval dashboard
- Metering
Ongoing
Growth iterations
New AI features shipped behind flags, models swapped when evals and costs favor it, and pricing tuned from usage data.
- Monthly releases
- Margin report
Prove the Core AI Job in a Week, Then Build the Product
Before any UI, we test whether models can do your product's main job well enough, on your examples, and show you the eval results.
Book a Quality Spike- MVP build after a one-week quality spike
- 3 to 6 weeks
- MVP build after a one-week quality spike
- Faster delivery with AI engineering
- 5x
- Faster delivery with AI engineering
- Code and IP ownership
- 100%
- Code and IP ownership
- Products shipped
- 100+
- Products shipped
Trustworthy AI Products
When AI is the product, its mistakes are your brand. We build these protections in from the first release.
Talk Through Your ProductRelease gates on evals
No prompt or model change reaches users unless it matches or beats the current version on your eval set.
Tenant data isolation
Each customer's documents, memory and fine-tuning data stay scoped to their workspace and are never mixed in retrieval.
Honest AI disclosure
Users can see when content is generated, where facts came from and how to correct the output.
Cost and abuse limits
Per-user rate limits, plan caps and abuse detection protect your margins from runaway usage and prompt attacks.
Founders on Building With Us
5.0Clutch
5.0GoodFirms- 5.0Google
- 4.9Upwork
I had the pleasure of working with Sajal Tech on a travel website, and it was an incredible experience from start to finish. They demonstrated professionalism, clear understanding of requirements, and excellent communication throughout.
Knowledgeable, efficient, and ahead of schedule. Will hire again.
They completed our project even when the scope changed slightly. They were cooperative when we wanted to add or modify features.
AI-Native Products by Market
The core job changes by market. Evals, metering and data isolation are built in every time.
Health and wellness
Coaches and companions that remember a person's history and adapt plans over time, with clinical topics handed to people.
- Personal coaching agents
- Report explanations
- Habit and plan adjustments
- Escalation to clinicians
Our AI-Native Product Stack
We choose tools that let a small team ship fast and change models later without a rewrite.
Models
- OpenAI GPT
- Claude
- Gemini
- Grok
- Liquid AI
App frameworks
- Next.js
- React 19
- Vercel AI SDK
- Vercel Eve
- Flutter
Agents and retrieval
- LangGraph
- Google Genkit
- pgvector
- Pinecone Nexus
Evals and analytics
- Promptfoo
- Braintrust
- Langfuse
- PostHog
Billing and metering
- Stripe Billing
- Stripe Meters
- Orb
- Lago
What an AI-Native Product Costs
Indicative starting prices. You get a fixed quote after a free scoping call, plus a model cost estimate per active user.
Option 1
AI prototype
Test your core AI idea with real users before a full build.
From
$2,500
Timeline
1 to 2 weeks
- Quality spike and eval set
- Working prototype
- Cost per task model
Option 2
AI-native MVP
A launchable product with accounts, billing and eval-gated releases.
From
$8,000
Timeline
3 to 6 weeks
- Core AI feature set
- Generative UI
- Usage metering and Stripe
- 30 days of support
Option 3
AI product platform
Multi-feature AI SaaS with agents, enterprise controls and scale.
From
$35,000
Timeline
3 to 6 months
- Agents as product features
- SSO and tenant isolation
- Experimentation and analytics
- Dedicated product team
AI-Native App Questions
An AI-enabled app adds a model to an existing workflow, such as a summary button. An AI-native app is designed around what the model can do, so the core workflow, interface and pricing would not exist without it. That changes how we design the product, test it and charge for it.
We build an eval set from real examples in the first week and agree on a pass rate target with you. Every release is scored against it, and features stay behind a flag until they meet the bar.
Most AI-native products mix a base subscription with usage, such as credits, tasks or documents processed. We model your cost per task early and build metering so you can test pricing without code changes.
It is an interface where the model returns structured output that the app renders as components, like a table, a form or a chart, instead of plain text. We define strict schemas so generated layouts stay valid, accessible and on brand.
They will. We put a model router between your features and providers, so you can switch or mix models when your evals and cost data say it is worth it, without rewriting product code.
A prototype with the real model takes 1 to 2 weeks. A launchable MVP with accounts, billing and eval gates takes 3 to 6 weeks, and our MVP in 3 Weeks package fits when the scope is tight.
Yes. We help choose the first use case, define quality, design the AI interaction patterns and plan pricing. Our team has shipped AI products across sales, health, compliance and commerce.
Plan limits, rate limits, caching, smaller models for simple steps and real-time cost dashboards per workspace. You see cost per active user next to revenue per user, so you can adjust plans early.
You own all code, prompts, eval sets, fine-tuning data and model configurations. Everything sits in your repositories and cloud accounts from day one, and the IP transfers to you on payment.
Let's build your next product together
Book a free strategy call and leave with a clear plan and estimate. No commitment.
- 01Pick a time that suits you
- 0230 minutes on scope, stack, timeline and budget
- 03Fixed-price proposal, NDA on request