Data science work that ships—and sticks.

Background

A global fintech unicorn with 10M+ users was overwhelmed by support tickets. Traditional chatbots could only answer FAQs, forcing 80% of queries to human agents.

Problem

Support costs were ballooning ($15M/year). Customers faced 48-hour wait times for simple actions like refund processing or account tier upgrades, leading to high churn.

Solution

We built a Level 4 Autonomous Agent capable of executing complex workflows. It doesn't just chat; it authenticates users, checks eligibility, and performs write-actions directly in the core banking system.

Data methodology

• Orchestrated a swarm of specialized agents (Triage, Auth, Action, Compliance) using LangGraph.
• Implemented "Tool Use" for direct API integration with Salesforce, Stripe, and internal ledgers.
• Designed a dynamic context window that retrieves user history and policy docs via RAG.
• Deployed a "Constitutional AI" guardrail layer to prevent hallucinations in financial advice.

Tools used

Python, LangGraph, GPT-4o, Pinecone, FastAPI, Kubernetes, Datadog

Results

• 75% of tickets fully resolved without human intervention.
• $8.5M annual savings in support operations costs.
• <3 seconds average time to resolution for complex account actions.
• CSAT score increased from 3.2 to 4.8/5.0.

Background

R&D scientists spent 40% of their time searching through 50 years of internal research papers, clinical trial logs, and FDA filings scattered across SharePoint and legacy drives.

Problem

Critical insights were lost in "data silos," leading to redundant experiments and delayed drug discovery pipelines. Keyword search was useless for conceptual queries.

Solution

We deployed a secure, multi-modal RAG system indexing 15 million documents. It understands chemistry formulas, tables, and handwriting, allowing scientists to "chat" with their entire collective knowledge.

Data methodology

• Built a hybrid search engine combining dense vector retrieval (semantic) and sparse keyword search (BM25).
• Implemented document-level security permissions (ACLs) to ensure data privacy.
• Fine-tuned embedding models on biomedical corpus for superior domain accuracy.
• Added "Citation Mode" where every answer links directly to the source paragraph and page.

Tools used

Weaviate, LangChain, LlamaIndex, Unstructured.io, Azure OpenAI, React

Results

• 12 hours saved per scientist per week in literature review.
• 30% acceleration in early-stage drug discovery phases.
• Zero data leaks verified by third-party security audit.
• Adoption by 4,000+ researchers globally within 3 months.

Background

Managing a global supply chain involves thousands of variables: weather, tariffs, port congestion, and demand spikes. Human planners were overwhelmed by the complexity.

Problem

Reactive decision-making led to massive detention/demurrage fees and stockouts. The company needed a system that could predict disruptions and act on them.

Solution

We built a "Digital Twin" of the supply chain powered by Reinforcement Learning agents. The system autonomously re-routes shipments and re-allocates inventory to optimize for cost and speed.

Data methodology

• Ingested real-time satellite data, shipping manifests, and weather APIs.
• Trained Multi-Agent Reinforcement Learning (MARL) models to simulate millions of scenarios.
• Connected agents to ERP systems to trigger purchase orders and carrier bookings automatically.
• Built a "Control Tower" dashboard for human oversight of high-value decisions.

Tools used

Ray RLLib, Python, Kafka, Snowflake, Tableau, AWS SageMaker

Results

• $42M reduction in annual logistics spend.
• 98% on-time delivery rate, up from 82%.
• Automated negotiation with carriers for spot rates, saving 15% on average.

Background

Radiologists and clinicians are burnt out, reviewing hundreds of scans daily. Misdiagnosis rates due to fatigue were a growing concern.

Problem

Doctors needed a "second pair of eyes" that could synthesize imaging data (X-rays, MRIs) with unstructured electronic health records (EHR) and genetic markers.

Solution

We developed a HIPAA-compliant Multi-Modal AI that analyzes images and text simultaneously. It generates differential diagnoses with confidence intervals and highlights regions of interest.

Data methodology

• Trained a Vision-Language Model (VLM) on 2M+ anonymized medical image-report pairs.
• Implemented "Explainable AI" (XAI) heatmaps to show doctors why the model made a prediction.
• Integrated with HL7/FHIR standards for seamless hospital workflow insertion.
• Achieved FDA "Software as a Medical Device" (SaMD) clearance readiness.

Tools used

PyTorch, MONAI, transformers, NVIDIA Clara, AWS HealthLake

Results

• 99.3% sensitivity in detecting early-stage anomalies.
• 60% reduction in radiologist review time per scan.
• Ranked #1 in internal blind tests against senior specialists for rare pathologies.

Background

In high-frequency trading, milliseconds matter. Traditional quant models relied on price history, missing the alpha hidden in unstructured news and social sentiment.

Problem

The fund wanted to capitalize on market-moving news events (earnings calls, geopolitical shifts) before the broader market reacted.

Solution

We engineered an ultra-low-latency NLP pipeline that ingests global news feeds, analyzes sentiment/impact, and executes trades in sub-millisecond timeframes.

Data methodology

• Built a custom BERT-based model optimized for financial jargon and "Fedspeak".
• Deployed on FPGA-accelerated hardware for microsecond inference speeds.
• Connected directly to exchange co-location centers to minimize network hops.
• Implemented rigorous backtesting on 10 years of tick-level data.

Tools used

C++, Python, PyTorch, FPGA, KDB+, Bloomberg API

Results

• 22% annualized alpha over the benchmark index.
• < 50 microseconds tick-to-trade latency.
• Scalable to process 100,000+ news items per second during market open.

Background

A mid-sized legal firm was manually reviewing thousands of supplier contracts during M&A due diligence, creating a massive bottleneck.

Problem

Lawyers spent 70% of their time on rote clause identification rather than risk analysis. Turnaround times were slow, and human error in spotting "change of control" clauses was a risk.

Solution

We built a multi-agent AI system using LangGraph where specialized agents (Parser, Analyst, Reviewer) collaborated to extract, classify, and flag high-risk clauses automatically.

Data methodology

• Ingested PDF/Docx files via OCR and layout-aware parsing.
• Orchestrated agents: one to extract text, one to check against risk playbooks, one to summarize.
• Used RAG to ground answers in the specific contract text, citing page numbers.
• Implemented "Human-in-the-Loop" breakpoints for low-confidence flags.

Tools used

LangChain, LangGraph, GPT-4o, Pinecone, Docker, React Frontend

Results

• 90% reduction in initial contract review time per document.
• Zero missed critical risk clauses in blind benchmark testing.
• Scalable to 10,000+ documents per day without adding headcount.

Background

An automotive parts supplier relied on visual inspection by human operators to catch surface defects on metal stampings.

Problem

Fatigue led to inconsistent inspection rates. Defect escapement to the customer was 3%, leading to costly returns and reputation damage.

Solution

Deployed an Edge AI computer vision system on the assembly line cameras to detect scratches, dents, and rust in real-time (30fps).

Data methodology

• Collected and annotated 5,000+ images of defective and clean parts.
• Trained a YOLOv8 object detection model optimized for inference speed.
• Deployed via TensorRT on NVIDIA Jetson edge devices for low-latency processing.
• Connected to PLC to automatically trigger reject chutes for bad parts.

Tools used

PyTorch, YOLOv8, NVIDIA TensorRT, OpenCV, MQTT, Edge Impulse

Results

• 99.5% defect detection rate, surpassing human accuracy.
• $1.2M annual savings in returns and rework costs.
• Real-time dashboard of defect types and frequency for root cause analysis.