CAPABILITIES

AI-Powered Apps & Products

From scope to production: applications where AI is the product, not a bolt-on. Our ad-production platform runs a 13-state production pipeline; our principal built 15 production AI apps at Newsweek.

PRACTICE NO.
02
PRINCIPALS
PA · NB
CASES ON FILE
02
FIELD
SAAS & DEVELOPER TOOLS · AI / ML INFRASTRUCTURE · MEDIA & PUBLISHING · DTC & RETAIL
AI-Powered Apps
FIG. 01 · NEIGHBORHOOD MAP · SOURCE: TEAM EXPERTISE DATASET

PROBLEM SPACE

Full-stack products with AI at the core, designed, built, and run end to end.

From scope to production: applications where AI is the product, not a bolt-on. Our ad-production platform runs a 13-state production pipeline; our principal built 15 production AI apps at Newsweek.

ROUTINE CLEARS AUTOMATICALLYEDGE CASES ROUTE TO A REVIEWER

WHAT WE DELIVER

  • 2.1product scoping & UX
  • 2.2full-stack build (Next.js / FastAPI / Postgres)
  • 2.3AI feature engineering
  • 2.4deployment & operations
  • 2.5iteration against real usage
15 PRODUCTION APPS · NEWSWEEK

HOW IT SHIPS

Systems in this practice run as a loop, not a launch. Inputs are served, routine outcomes execute automatically, and anything consequential holds at a review gate where a named person clears it with context attached. Every decision (human or automatic) lands in the audit trail.

INPUTSERVEGATEEXECUTEAUDITHUMAN REVIEW · CONSEQUENTIAL ONLY
THE ENGAGEMENT MODEL BEHIND THIS →

PROOF

15
production AI appsNEWSWEEK · 5 YEARS
13
pipeline states in one productIN-HOUSE AD PLATFORM
CASE FILE 01 · AD PLATFORM (IN-HOUSE) · AI VIDEO AD PRODUCTION PIPELINE · 5× WEEKLY AD OUTPUTCASE FILE 02 · NEWSWEEK · DOCUMENT INTELLIGENCE AT NEWSROOM SCALE · 30M-DOC SEARCH

SELECTED PROJECTS · CLIENTS ANONYMIZED

A visual strategy builder that backtests before it tradesA retail algorithmic-trading platform
EVERY FIGURE TRACES TO SHIPPED WORK

WHO BUILDS IT

Parik AhlawatPrincipal · Agents & OrchestrationMulti-agent platforms, model routing, n8n orchestration. 618+ automations shipped across 12 industries.PROFILE →Nikhil BeryPrincipal · RAG & Document AIRAG, document intelligence, multi-LLM pipelines, vector + graph search at scale. 30M-doc Elasticsearch indexes.PROFILE →

FIELD NOTES

Private AI, in-house: the shift to internal models and agentic infrastructure6 MIN READThe AI app you ship in eight weeks: what production-ready means5 MIN READ

ADJACENT

Asked about this practice

Questions we get.

The same answers we give on a first call about this practice.

  • FAQ-01What does it mean for AI to be the product, not a bolt-on?

    The application is designed around the AI from scope to production, not a chatbot stapled onto an existing screen. We build full-stack on Next.js, FastAPI, and Postgres, and one of our principals shipped 15 production AI apps at Newsweek over five years.

  • FAQ-02How complex a product can you run end to end?

    Our own ad-production platform runs a 13-state production pipeline as one product, from brief to delivered ad. We scope, build, deploy, and then iterate the app against real usage rather than handing over a prototype.

  • FAQ-03Who builds it, and do juniors touch it?

    Named principals build it. No juniors on production systems. You see the product running on your data in weekly demos, not a slide deck of what it might one day become.

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Tell us the use case. One call is enough to scope whether there is a fit, and what it takes to ship.

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