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AI-powered tooling & automation.

AI features that survive contact with production — with evaluation, fallbacks, and cost control designed in, not bolted on.

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Overview

The gap between an AI demo and an AI feature is everything that happens when the model is wrong: evaluation, fallbacks, human review paths, and cost ceilings. This service builds AI capabilities into real products — automation pipelines, LLM-backed workflows, intelligent crawlers — with that unglamorous machinery included.

I built GhostAI, an AI crawler that parses DOMs and auto-generates test cases for a QA platform, and I architect the backend of an AI-powered telephonic enrollment platform handling US healthcare-assistance workflows. Both taught the same lesson: the model is 20% of the work, and the honest feasibility call up front is worth more than either.

What's included

Scope of the service

Feasibility scopingA timeboxed, evidence-based answer to 'can AI actually do this?' — on your data, before you commit a budget to it.
  • ▸Spike against your real data with an accuracy target agreed up front
  • ▸Approach comparison: LLM vs rules vs hybrid, with cost per unit at volume
  • ▸A written go/no-go recommendation — including 'no'
DELIVERABLEFeasibility report with real numbers, in one to two weeks.
LLM & model integrationModel calls wired into your existing backend with prompt management, structured outputs, and versioning — not a notebook in production.
  • ▸Structured outputs with schema validation on every model response
  • ▸Prompts versioned, tested, and reviewed like code
  • ▸Provider abstraction so you can switch models without a rewrite
DELIVERABLEA production integration, not a notebook.
Automation pipelinesCrawling, DOM parsing, extraction, and generation workflows built on Scrapy, Celery, and WebSockets for real-time feedback.
  • ▸Crawling and DOM parsing with throttling and per-source failure isolation
  • ▸Extraction and generation workflows on Celery with retries
  • ▸Real-time progress over WebSockets for long-running jobs
DELIVERABLEPipelines processing your real workload on staging.
Evaluation & fallbacksAccuracy measured against a golden set; defined behavior for low-confidence outputs — human review, graceful degradation, or refusal.
  • ▸Golden evaluation set built from real cases, run in CI on every change
  • ▸Confidence thresholds with defined behavior below them
  • ▸Human-in-the-loop review queue where the stakes require it
DELIVERABLEAn eval harness — the difference between impressive and dependable.
Cost & latency controlCaching, batching, and model-tier routing so the unit economics still work at 100x volume.
  • ▸Caching and batching for repeated or bulk operations
  • ▸Model-tier routing: cheap models for easy cases, expensive ones only when needed
  • ▸Hard cost ceilings with alerts before the bill surprises you
DELIVERABLEUnit economics that survive 100x volume, documented.
Monitoring for driftDashboards tracking output quality over time — AI features degrade silently; yours will tell you.
  • ▸Output-quality dashboards tracking accuracy against the golden set over time
  • ▸Alerts when quality or cost drifts past thresholds
  • ▸User feedback captured and wired back into the eval set
DELIVERABLEA feature that tells you it's degrading — before your users do.
How it runs

Phases specific to this service

These slot into the standard engagement process — discovery, requirement analysis, and a written proposal always come first.

1

Feasibility spike

1–2 weeks, timeboxed

A narrow prototype against your real data with a measurable target. Ends with numbers and a go/no-go — including 'no', which costs you two weeks instead of two quarters.

2

Prototype to pilot

2–4 weeks

The winning approach is built into a pilot with real users in the loop, capturing corrections that become the evaluation set.

3

Productionize

Weekly cycles

Evals in CI, fallback paths, rate and cost limits, and monitoring — the feature graduates from 'impressive' to 'dependable'.

4

Measure & iterate

Ongoing

Output quality tracked against the golden set; prompts and models updated on evidence, not vibes.

Proof

Where I've done this before

Shipped work this service is based on — details on the projects page.

Architect
GhostAI — AI test-case generationAI crawler that parses application DOMs and auto-generates test cases for a QA automation SaaS.
Backend Architect
Social Benefits Enrollment PlatformBackend of an AI-powered telephonic platform for Medicaid / SNAP / healthcare-assistance enrollment.
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Fit

Is this the right service?

GOOD FIT IF
  • ▸A workflow eats hours of repetitive human effort on text, documents, or web data
  • ▸You want an AI feature but need an honest feasibility answer first
  • ▸You have an AI demo that works — and no path to production
NOT A FIT IF
  • ·You want to train foundation models — I integrate and productionize, I don't do ML research
  • ·The AI is for the press release, not the product

ENGAGEMENT · Starts with the timeboxed feasibility spike as a small fixed-scope engagement; production build follows as a project or embedded work.

Other services
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Contact

Sound like your problem?

Send a short description of what you're building and where it hurts. Discovery call is free; written proposal within a week of requirement analysis.

Email meAll services
[email protected] · +91 79862 35112