MVP AI SaaS Mobile Pricing
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Ship useful AI features without turning your product into a science project

AI copilots, document workflows, structured outputs, agents, and automations integrated into real products with cost and failure controls.

Delivery model
21d

Focused sprint, production handoff, 30 days support.

AI Integration

AI copilots, document workflows, structured outputs, agents, and automations integrated into real products with cost and failure controls.

AI features connected to your product data
Structured outputs and validation
Prompt, model, and cost strategy
Logging and fallback paths
User-facing workflows that feel reliable

From scope to shipped product

01

Define the job the AI feature must perform

02

Choose model, retrieval, and workflow architecture

03

Integrate, test, and instrument the feature

04

Deploy with basic monitoring and handoff notes

Built for handoff

Everything is shaped around a product you can keep operating after launch.

OpenAI/Claude/Gemini integration
RAG or vector search where needed
Structured outputs
Retry and fallback logic
Cost-aware implementation
Observability notes

Who should use this

SaaS founders adding AI workflows
Teams automating repetitive operations
Products that need document, chat, or recommendation features

Pragmatic technology choices

OpenAI, Anthropic Claude, Gemini, LangChain/LangGraph when justified, Supabase/Postgres, vector search, n8n-style automation patterns.

Relevant project snapshots

ZalkAI

Productivity

AI-powered task manager inside WhatsApp with natural language input, smart scheduling, and team collaboration.

Launched in 19 days

Elete Basketball

SportsTech

AI-powered basketball training app with HD video drills, performance analytics, and virtual competitions.

4.6-star rating, thousands of users

Common questions

Can you add AI to an existing product?

Yes. We can integrate AI into an existing app if the product has usable data, clear workflows, and API access.

How do you control AI cost?

We use model selection, rate limits, logging, retries, and smaller models where they are good enough for the task.

Bring the product direction. We will shape the sprint.

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