AI App Development
AI App Development: Turn AI into a useful part of your product.
Build an AI-powered application or add intelligence to software your business already uses. Start with a real workflow, define what a good result looks like, and engineer the system around it.
Free 20-minute scoping call · Direct with Chirag
01 / Scope
What we can build together
LLM integrations
Add summarization, drafting, classification or structured data extraction. Validate model responses before they reach your users or database.
AI agents
Connect an assistant to specific tools and actions. Set permission boundaries, require approval for consequential actions, and handle failed steps.
Retrieval-augmented generation (RAG)
Help users find answers in your documents and knowledge base. Retrieve relevant material, show source references, and respect access permissions.
Intelligent automation
Reduce repetitive work such as sorting requests or preparing reports. Combine predictable business rules with AI where interpretation is needed.
AI-powered SaaS applications
Build the product around the AI feature: accounts, usage limits, billing when scoped, saved results and an interface people can understand.
Existing-system integrations
Connect AI workflows to your APIs, CRM or internal tools. Keep credentials on the server and design retries without duplicating business actions.
02 / Practical problems
Start with the work you want to improve
Your team keeps searching through documents
A permission-aware knowledge assistant can surface relevant passages and references. First check that the source material is current and searchable.
Manual processing slows down operations
Extract fields from incoming text, check required values, and route uncertain results to a person instead of silently accepting them.
Your prototype gives inconsistent answers
Build an evaluation set from real examples. Compare prompts and models against expected outcomes, then add fallbacks and clear failure messages.
AI costs are hard to predict
Estimate usage per workflow, cap request sizes, apply rate limits, and monitor provider spend before expanding access.
03 / Delivery
From discovery to deployment
- 01
Discovery and feasibility
Map the user, business task, available data and success criteria. Decide whether AI adds value and identify privacy or integration constraints.
- 02
Prototype and evaluate
Test the riskiest AI workflow on representative examples. Review accuracy, response time and expected usage costs before committing to the full build.
- 03
Build the application
Implement the agreed interface, backend and integrations. Review AI-assisted code, test permissions and validate outputs alongside ordinary application logic.
- 04
Deploy and hand over
Verify critical journeys, configure monitoring and usage limits, and deploy into your accounts. Document setup, operating costs and the agreed support scope.
04 / Engineering judgment
AI capability, backed by software engineering.
I bring 6+ years of software engineering experience to architecture, data modeling, frontend and backend delivery. AI-assisted development speeds up exploration and repetitive coding; code review, testing and clear acceptance criteria remain part of the work.
Model output is not a guarantee of correctness. The application needs explicit boundaries: what data a model can access, which actions it can take, when a human reviews a result, and what happens when a provider is unavailable.
Technical foundation and tools
My full-stack foundation includes TypeScript, React, Next.js and Angular. Depending on scope, the implementation can use model-provider APIs, embeddings, vector search, a relational database and your existing APIs. Provider and hosting choices follow data requirements, budget and the systems you already operate.
See the full-stack work
RetroYugi is a community platform with a searchable card database, decklists and custom game rules. Its application structure and data modeling show the full-stack foundation I bring to this work. Explore the portfolio →
05 / Before we start
AI App Development questions
How much does AI app development cost?
The price depends on the workflows, data preparation, integrations and application features. After discovery, I define the deliverables and estimate the build. Model usage, hosting and third-party subscriptions are separate operating costs that we discuss before implementation.
How long will it take to build?
A focused integration has a different scope from a new SaaS application. Data readiness, evaluation needs and external systems affect the timeline. We agree on milestones after reviewing the workflow; a prototype helps resolve uncertain parts before a launch date is committed.
How do you handle security and private business data?
We identify sensitive data before choosing a provider. The implementation can include server-side credentials, access checks, data minimization and controlled logging. Provider retention settings and any regulatory requirements need review for your use case; security requirements belong in the agreed scope.
Can you add AI to my existing software?
Yes, subject to reviewing the codebase, available APIs and access permissions. A contained integration is often a useful starting point. I assess compatibility and explain any backend or data changes needed before proposing the implementation.
Do you provide ongoing support?
We agree on post-launch fixes, maintenance and monitoring before work starts. Continued support can cover model or API changes, evaluation updates and new workflows. Its scope and cost are defined separately so responsibilities stay clear.
06 / Next step
Which workflow should AI improve first?
Bring the business task, a sample input if you have one, and the software it needs to connect to. We can discuss feasibility and a sensible first scope on a free 20-minute call.
Discuss your AI application →