Live still — ASAI
Document AI
Structured extraction from raw unstructured documents with specialized prompts, confidence scoring, and taxonomy normalization.
Case 01Live
Case study · Our own product
ASAI — a production AI platform, built and run by us.
The clearest way to judge a studio is to see what it has actually shipped and operates. ASAI is ours — an AI job-discovery platform for tech professionals, live in production, with real ML doing real work.
The challenge
Job search is noisy, opaque, and full of dead listings.
Candidates drown in keyword-matched, often-fake postings with no signal about whether a company is really hiring. The goal: give candidates the intelligence recruiters have always had — genuine fit, verified roles, and real hiring signals.
What we shipped
A verification layer, a fit engine, and a self-feeding data pipeline.
Real-time verification against company career pages, a multi-signal fit-scoring model, LLM resume parsing, and an agentic crawler fleet — delivered across responsive web and native mobile apps.
Under the hood
Six real systems, running in production.
Not one LLM call in a wrapper. ASAI runs multiple distinct ML systems — the same depth we bring to your build.
01
Document AI
Structured extraction from raw unstructured documents with specialized prompts, confidence scoring, and taxonomy normalization.
02
Semantic matching
High-dimensional vector embeddings + mathematical similarity against a domain taxonomy, with NLP preprocessing and confidence thresholds.
03
A bespoke ranking model
A probability-of-fit scorer with custom mathematical weighting — non-linear skill curves, experience decay, freshness weighting — not a simple wrapper.
04
Agentic web scrapers
An AI agent that ingests raw markup and dynamically generates parser blueprints for unknown sources, with model fallback and retries.
05
A crawler fleet
Enterprise data sources ingested, standardized, and verified in real time, with automated discovery and fingerprinting.
06
Shipped cross-platform
Responsive web + native mobile from a shared typed core, running on containerized microservices with full system telemetry.
Architecture & scale
Dockerized microservices, a universal crawler, real observability.
Independently deployable services on AWS, a data-driven crawler that scales to new sources without new code, scheduled background jobs, and a custom monitoring stack — the production discipline your build inherits.
- Modern Web Frameworks
- Cross-Platform Native Core
- High-Performance Microservices
- Scalable Data Storage
- Multimodal LLMs
- Vector & Embedding Models
- Containerized Cloud Infra
- Real-Time Telemetry & APM
The trade-offs
Every architecture is a set of bets. These are ours.
Bet 01
Structured prompts & schema constraints over fine-tuning
Document extraction needs change frequently as data sources evolve. Structured prompt and schema iteration ships rapidly, whereas fine-tuning requires slow retraining loops.
What it costs us — Rigorous prompt-regression testing and schema validation pipelines.
Bet 02
High-dimensional vector similarity over live LLM evaluation
Matching runs continuously for every user query. Vector similarity is sub-millisecond, cost-effective, and fully explainable against our taxonomy.
What it costs us — Requires dedicated NLP preprocessing and confidence threshold tuning.
Bet 03
Custom scoring algorithms over off-the-shelf ranking engines
Mathematical scoring, time-decay curves, and relevancy weighting stay transparent and explainable to users.
What it costs us — Engineered and maintained in-house without relying on external black-box services.
Bet 04
Agent-generated parser blueprints, not static scrapers
An autonomous agent that creates parser blueprints per source scales seamlessly without constant manual engineering.
What it costs us — Requires automated validation gates, fallback strategies, and retry handlers.
Bet 05
Containerized microservices over serverless execution
Continuous ingestion pipelines represent steady background loads — dedicated compute provides predictable cost and guaranteed performance.
What it costs us — Requires active infrastructure management, orchestration, and system monitoring.
Bet 06
Custom domain metrics & telemetry over generic SaaS APMs
Domain-specific signals — extraction confidence, ingestion success, match drift — matter far more than basic uptime checks.
What it costs us — Telemetry dashboards and alerting rules are designed and tuned specifically for the platform.
We'll build yours with the same rigor.
Everything you just read, we designed, built, and operate ourselves. That's the team you'd be hiring.