AI product designer & developer · Texas, USA

Prasiddha K.AI systems builder.

I design AI products that retrieve, reason, act—and show their work.

From retrieval-powered products and agentic workflows to multimodal tools, evals, safeguards, and the interface in between.

Live AI system / HeatSignal

Search anywhere.
Read the risk now.

A working environmental-intelligence demo: resolve any searchable location, pull current public weather and modeled air-quality data, calculate heat risk, and expose every step of the workflow.

Latest available data workflowGlobal search · Public data
01Locate
02Weather
03Air quality
04Classify
05Cite
HEATSIGNAL / READY

Search cities and postal codes worldwide to turn latest-available weather and modeled air-quality data into a transparent heat-risk snapshot.

Weather and geocoding: Open-Meteo. Air quality: Open-Meteo using CAMS global atmospheric forecasts. Heat index: U.S. National Weather Service method.

Portfolio demonstration only. Values may be modeled or delayed and are not medical advice or an official warning. Follow local authorities, the National Weather Service, and AirNow for official guidance.

What I build

AI systems,
end to end.

A useful AI product is more than a model call. It needs reliable context, appropriate tools, clear permissions, measurable quality, and a thoughtful role for human judgment.

01

Retrieval & knowledge

Grounded answers over documents and structured data—with ingestion, hybrid search, citations, permissions, and freshness designed into the experience.

02

Agentic workflows

Tool-using agents that plan, act, retry, and hand off with explicit state, scoped permissions, approval gates, and reliable fallbacks.

03

Multimodal AI

Interfaces that combine text, images, audio, and structured inputs so people can work naturally across messy real-world information.

04

Evals & safety

Task-based eval sets, tracing, failure analysis, guardrails, and review queues that reveal when a system is uncertain or needs a person.

Independent product lab

Selected AI work

01 — 04

Four working public-data products that turn live evidence into useful decisions—without hiding the source, freshness, limitations, or role of human judgment.

Live public dataIndependent exploration
/01

Voltline

Latest Texas grid intelligence from federal hourly data

Voltline turns EIA-930 hourly submissions for Texas into a clear view of demand, day-ahead forecast, net generation, total interchange, and recent load movement.

01Query EIA-930
02Normalize signals
03Align forecast
04Explain variance
Latest public data · U.S. Energy Information Administration

Texas grid signals monitor

EIA-930 hourly reporting
Texas balancing authority · ERCODemand, forecast, generation, and interchange in one explainable view
01Query EIA-930
02Normalize signals
03Align forecast
04Explain variance

Connecting to the latest available EIA-930 data for Texas…

Source: U.S. EIA Hourly Electric Grid Monitor ↗ · cached for ten minutesEIA-930 data is preliminary and can be delayed, revised, imputed, or missing. Voltline is not an alerting, reliability, dispatch, reserve-margin, or emergency-guidance system.

How the AI works

A cached read-only service retrieves the public EIA Hourly Electric Grid Monitor feed for ERCO, validates the payload, aligns demand with forecast and other signals by reporting hour, and applies transparent variance and recent-range logic.

Human control & safety

The interface never calls a public-data pattern a reliability alert, reserve margin, or dispatch recommendation. Data latency and official operator ownership stay visible.

How I would evaluate it

Source freshness · schema validation · timestamp integrity · graceful recovery

Live public dataIndependent exploration
/02

HeatSignal

Live environmental intelligence for heat-aware decisions anywhere

HeatSignal turns a city, region, or postal code into a current, source-backed snapshot of temperature, heat index, air quality, and practical next steps.

01Resolve the place
02Fetch weather + AQI
03Classify heat risk
04Cite + guide

How the AI works

A client-side orchestration flow geocodes the search, retrieves current weather and modeled air-quality data in parallel, calculates the NWS heat index when conditions meet its threshold, classifies risk, and keeps the data sources visible.

Human control & safety

Modeled data is labeled honestly, missing values remain visible, no personal health outcome is predicted, and official NWS, AirNow, and local guidance stays one click away.

How I would evaluate it

API integrity · location accuracy · calculation tests · failure recovery

Live public dataIndependent exploration
/03

FieldLens

A public-data vehicle safety and recall research assistant

FieldLens turns a 17-character VIN into a readable vehicle identity, matching federal recall campaigns, urgent Park It or Park Outside flags, and safe next steps.

01Validate the VIN
02Decode vehicle
03Retrieve campaigns
04Build safety brief
Live public data · National Highway Traffic Safety Administration

VIN safety and recall research assistant

No VIN is stored here

A public example VIN is preloaded. Replace it with a VIN you are authorized to check; it is sent directly to NHTSA only when submitted.

01Validate VIN
02Decode vehicle
03Retrieve campaigns
04Build safety brief

Enter a VIN to turn NHTSA identity data and matching recall campaigns into a readable safety brief.

Sources: NHTSA vPIC ↗ and NHTSA Recalls ↗This lookup finds campaigns by decoded year, make, and model; it does not determine whether a specific VIN has an incomplete recall or whether a vehicle is safe to drive.

How the AI works

A two-stage retrieval workflow validates and decodes the VIN through NHTSA vPIC, uses the decoded year, make, and model to retrieve federal recall campaigns, then structures official issue, consequence, and remedy text into a prioritized brief.

Human control & safety

A year/make/model match is never presented as VIN-specific open-recall status. The product sends the VIN only to NHTSA, stores nothing locally, and routes final confirmation to NHTSA or a dealer.

How I would evaluate it

VIN validation · campaign recall · warning fidelity · empty/error states

Live public dataIndependent exploration
/04

SceneCraft

A live website readiness and brand experience auditor

SceneCraft inspects any public website and turns its live HTML, metadata, crawl signals, response headers, and Mozilla security grade into a transparent repair queue.

01Fetch public page
02Inspect signals
03Score readiness
04Prioritize fixes
Live public website inspection · no login required

Website readiness and brand experience audit

Static preflight + MDN security

The audit reads public HTML, metadata, crawl files, response headers, and a Mozilla Observatory security grade. It does not log in or submit forms.

01Fetch public page
02Inspect signals
03Score readiness
04Prioritize fixes

Enter any public website to turn its live technical signals into a transparent, prioritized improvement brief.

Method: live static preflight plus MDN HTTP Observatory ↗This is directional—not a Lighthouse/Core Web Vitals measurement, security certification, legal SEO review, or WCAG conformance test. Scores can change when the public page changes.

How the AI works

A protected server workflow fetches only public pages, follows validated redirects, inspects search, accessibility, social, and delivery signals, adds MDN HTTP Observatory evidence, and ranks concrete fixes by impact.

Human control & safety

Local and private-network addresses are blocked, redirects are revalidated, page size and time are bounded, and the product clearly separates a directional static audit from Lighthouse, WCAG, security, or legal certification.

How I would evaluate it

Check precision · URL safety · response limits · recommendation usefulness

Every product above retrieves latest-available public data and shows its method. HeatSignal uses Open-Meteo and NWS heat-index logic; Voltline uses EIA-930; FieldLens uses NHTSA; SceneCraft inspects a submitted public page and adds Mozilla Observatory results. Data can be delayed, revised, incomplete, or unavailable. These are independent products—not client engagements, emergency guidance, professional advice, or production performance claims.

How I work

Design the loop,
not just the prompt.

The strongest AI experiences make context, state, uncertainty, permissions, and human judgment visible—not magical.

01

Frame

Define the user decision, source of truth, acceptable failure, and what should remain human.

02

Ground

Connect trusted context, tools, and permissions. Make citations and provenance part of the product.

03

Prototype

Build the smallest end-to-end workflow and make the system’s state visible in the interface.

04

Evaluate

Test quality, cost, latency, safety, and failure modes against representative tasks and a non-AI baseline.

05

Improve

Add feedback loops, human escalation, monitoring, and versioned changes before expanding autonomy.

Start with the problem

Building
with AI?

If you’re exploring a knowledge product, an agentic workflow, a multimodal experience, or a role where this thinking matters, I’d like to hear what needs to work—and what cannot fail.

Available for focused product design, rapid prototyping, and AI system collaboration from Texas, USA.

Send a project inquiry