Structured extraction
Turn unstructured text or images into product data that normal code can validate before the workflow continues.
[ENG-SERVICES] // AI Product Integration
Add useful AI to a real product, not just a demo.
I integrate practical AI workflows into mobile and web products: structured extraction, OCR and document processing, conversational interfaces, classification, summarization, embeddings, semantic search, and backend orchestration.
The work includes validation, failure handling, latency, cost, and product UX because a model response is only one part of a production feature.
AI should perform a useful job inside the product and remain usable when the model behaves imperfectly.
01 // Good use cases
Start with a useful job, not a generic chatbot.
AI is useful when it improves a defined product workflow and the quality of its contribution can be reviewed or measured.
OCR and document processing
Extract useful information from receipts or documents inside a controlled workflow with review and correction where needed.
Conversational interfaces
Use natural-language interaction when conversation makes a specific product task easier—not as decoration on every screen.
Semantic search and embeddings
Retrieve relevant information by meaning where exact keyword matching does not serve the user's task well.
Classification and summarization
Reduce repetitive interpretation work when expected output and acceptable quality can be defined.
Workflow assistance and product signals
Help users complete a defined task or derive structured signals from input, with appropriate validation before downstream use.
02 // Integration capability
Production behavior around the model matters most.
A useful integration connects model behavior to data, product states, backend boundaries, and deterministic downstream rules.
Model interaction
Prompt and workflow orchestration, structured output, streaming where appropriate, and provider or model integration.
Data transformation
OCR, extraction, schemas, classification, embeddings, retrieval, and validation of data entering the product.
Product integration
Loading, error, review, and correction states with user confirmation and deterministic downstream behavior.
Backend orchestration
Server-side provider calls, authentication, secrets, logging, rate limits, usage controls, and caching where appropriate.
03 // How an engagement works
Define the job before choosing the model.
Experimentation and production integration are different stages. The process keeps the product requirement visible through both.
STAGE 01
Define the job
Clarify the task, why AI is appropriate, inputs, required output, wrong-answer behavior, acceptable latency, and how success will be evaluated.
STAGE 02
Prototype the workflow
Evaluate model suitability, output consistency, prompt and schema design, edge cases, likely latency, and cost.
STAGE 03
Integrate into production
Build the backend boundary, authentication, validation, appropriate retries or fallback, UI states, logging, and usage controls.
STAGE 04
Measure
Where relevant, observe completion, correction, failure rate, latency, cost, adoption, and the product outcome without inventing success metrics in advance.
04 // Production reliability
Model output is an input, not an authority.
AI cannot be made infallible. The product can still behave deliberately when output is wrong, slow, malformed, refused, or unavailable.
Never trust model output blindly
Structured output should be checked against a schema and product constraints before it becomes authoritative data.
Design for failure
Timeouts, malformed responses, refusals, low-quality output, unacceptable latency, and provider limits need explicit product behavior.
Keep deterministic rules deterministic
Calculations and business rules that normal code can perform reliably should stay in normal code.
Provide correction paths
When output affects a user's workflow, the experience should support review and correction where the consequence requires it.
Use fallbacks intentionally
Fallback behavior should reflect the product consequence rather than blindly retrying or switching models.
05 // Backend and security boundary
Keep provider access behind the product boundary.
Provider API keys and sensitive secrets belong server-side, not inside mobile or web clients. Model access should pass through a backend boundary appropriate to the product.
Authentication and authorization still apply. Logging should avoid exposing sensitive input, while rate limits and usage controls should match the risk and expected usage.
- Server-side provider credentials
- Authenticated and authorized access
- Sensitive-input-aware logging
- Usage controls and rate limits where relevant
06 // Operational reality
Cost, latency, and failures become product behavior.
Production design considers the work around each call without publishing arbitrary targets or treating one provider as universally best.
Cost and repeat work
Consider model cost, input size, unnecessary repeated calls, and caching when reuse is safe and appropriate.
Latency and timeouts
Set expectations in the interface and define what happens when a response takes too long or a retry would make the experience worse.
Usage visibility
Keep enough visibility into usage, failures, and provider behavior to understand operational impact without leaking sensitive content.
Failure monitoring
Distinguish malformed output, provider errors, limits, validation failures, and user correction so the right problem can be addressed.
FEATURED // Relevant work
AI-assisted workflows grounded in product behavior.
The examples below are intentionally limited to approved product-level facts. They do not expose private prompts, provider configuration, or verification-sensitive architecture.
Mobile Product Development · AI Product Integration
SettleTab
SettleTab is a live Android app for itemized shared bills. People can scan a receipt or enter items manually, correct the bill, assign individual and shared purchases, calculate what each person owes, and share a read-only result.
AI/OCR-assisted structured extraction inside a mobile workflow, with editable results and deterministic bill-splitting logic.
Shashank Sangule · Founder & Lead Developer, DivWeavers
AI Product Integration · Mobile Product Development
Tare
Tare is a mobile habit reflection app that saves original check-ins, proposes structured fields for user review, and uses recorded history for progress views and insight paths.
A written or transcribed reflection becomes a proposed structured check-in for user review; the original entry is saved first.
Shashank Sangule · Founder & Lead Developer, DivWeavers
FAQ // Common questions
AI integration FAQs
Can you add ChatGPT or LLM functionality to my app?
Yes, when an LLM is a suitable way to perform a defined product task. The provider and model follow the workflow, reliability, latency, and cost requirements.
How do you decide whether AI is appropriate?
I start with the user job, required output, acceptable error, latency, and value. If deterministic code or ordinary search solves it better, that is the better recommendation.
Can AI output be trusted?
Not blindly. Structured output should be validated, important consequences should have deterministic checks, and users should have correction paths where appropriate.
Can you work with existing models or APIs?
Yes. I can integrate an existing provider or model when it fits the product, or help evaluate alternatives against the actual workflow.
Can you add AI to an existing product?
Yes. Existing mobile and web products are a strong fit when there is a clear workflow to improve and the current architecture can support an appropriate backend boundary.
Can you handle the backend integration too?
Yes. I can implement server-side provider access, authentication, validation, schemas, logging, rate limits, usage controls, and product-facing endpoints within scope.
How do you handle failure or malformed output?
The integration validates expected structure and defines UI and backend behavior for timeouts, provider errors, malformed output, refusals, and low-quality results.
How do you think about cost and latency?
They are product constraints. Model choice, input size, repeated calls, caching, timeouts, and the interaction design should be evaluated against the value of the workflow.
Can you work with OCR or structured extraction?
Yes. I can build OCR-assisted extraction workflows that convert unstructured input into validated schemas with review and correction where needed.
Do you build RAG, embedding, or semantic-search workflows?
Yes, where retrieval by meaning materially improves the product. The data source, evaluation approach, authorization boundary, and failure behavior need to be part of the design.
Have a product workflow that AI might improve?
Describe the product, the workflow, and what the AI should help the user accomplish. You do not need to choose a model or provider before getting in touch.