AI Engineering · Industrial Systems · Production Infrastructure
Engineering intelligence
from bits to atoms.
We architect AI systems that work at every layer.
Trusted by engineering teams at
Selected Work
Case studies
Industrial Simulation
Hydrogen Digital Twin
Hygenco Green Energies
Green hydrogen proposals took weeks. Engineers ran ANSYS simulations for every plant configuration. We built a proposal engine using PINNs to learn the simulation manifold with physics constraints baked into the loss function.
CFD Data → PINN Training → Physics Loss
↓ ↓ ↓
[ANSYS] [PyTorch] [∇²u = f]
↓ ↓ ↓
Ground Surrogate Constraint
Truth Model SatisfiedContent & Growth
Automated Content Pipeline
Pick Your Trail
Travel content couldn't scale. Hiring writers was slow. We automated the content pipeline with human-in-loop editing and custom lead scoring linked to on-site behavior.
Topic → Research → Draft → Review → Publish ↓ ↓ ↓ ↓ ↓ [SEO] [Web] [Claude] [Human] [CMS] ↓ ↓ ↓ ↓ ↓ Keywords Sources Content Approved Live
Quantitative Finance
Alpha Generation
WorldQuant
Finding uncorrelated signals in noisy financial data. We built ensemble ML models with walk-forward validation and deep neural nets for time-series pattern recognition.
Data → Features → Ensemble → Signals ↓ ↓ ↓ ↓ [OHLCV] [Technicals] [XGB+LSTM] [Alpha] ↓ ↓ ↓ ↓ Raw Engineered Walk-fwd Uncorr
Agentic Systems
Workflow Orchestration
Under NDA
Multi-step workflow with dozens of API calls. LangChain agents looped and costs were unpredictable. We modeled it as an MDP with custom orchestration using MCP protocol.
Task → Plan → Execute → Validate ↓ ↓ ↓ ↓ [PRD] [DAG] [MCP] [Schema] ↓ ↓ ↓ ↓ Intent Steps Tools Output
The Challenge
Where AI systems break down
Production AI faces challenges that demos never show. We specialize in solving the hard problems that emerge at scale.
Edge deployment constraints
Local TTS and VLM quality lag behind cloud, and multilingual coverage remains uneven for privacy-first deployments.
Latency budget misses
Cold starts and multi-hop pipelines blow latency budgets, making real-time scaling unpredictable.
Orchestration reliability
Agentic workflows loop or break because outputs aren't deterministic. Debugging is painful.
Static evaluation metrics
Teams want evals that update from user feedback and business KPIs, not just accuracy scores.
Provider API instability
Provider APIs ignore parameters and rate-limit unpredictably. Robust fallbacks are essential.
Content drift
Generative UI and content pipelines drift toward hallucinations without tight constraints.
Our Edge
Production-grade patterns
that CTOs trust.
We've shipped these patterns across industrial AI, voice agents, and high-throughput RAG systems. Every technique here has survived production traffic and cost audits.
Technical Patterns
Model Distillation
We distill large model completions into fine-tuned 7B/8B models. Production inference that's actually affordable.
Lower cost · Faster turns · Similarity validated in evals
Dual Guardrails
Input filters catch safety issues before inference. Output gates enforce JSON structure with iterative re-ask loops. Parse failures caught before responses ship.
Two-layer safety · Structured outputs · Pre-response validation
Voice Latency Stack
Self-hosted STT, LLM, and TTS in a single cluster. Deepgram for transcription, Gemini Flash for generation. Target sub-500ms turns in dedicated, colocated clusters.
Dedicated clusters · Colocated inference · Latency budgets enforced
Prompt Caching
Aggressive caching for long, stable system prompts. Custom adapters when LiteLLM and DSPy don't expose cache_control natively.
Up to 50% inference cost reduction
Multi-Instance Routing
Requests spread across data centers to smooth rate limits and latency spikes. Automatic failover when providers misbehave.
Rate limit smoothing · Latency spike mitigation
Variability-Based Model Selection
High-variance inputs route to larger models. Controlled contexts use smaller, faster models. Task splitting without orchestration drag.
Right-sized inference · Reduced debugging overhead
Agent Platform
Four agents. Three flows.
Not isolated tools—an integrated system. Each agent specializes, but they work together through MCP bridges to handle end-to-end workflows.
Content Generation
TOFU content from industry signals. 5 templates, MDP-based scoring, platform-specific formatting for Twitter, LinkedIn, Reddit.
CRM & Sales
Lead tracking with temporal state. Call transcription, cohort segmentation, journey health scoring, real-time requirement extraction.
Orchestration
Deterministic workflows with budget enforcement. Guardrails for injection, loops, secrets. Human review gates, full observability.
Adversarial QA
8 personas stress-test UIs: Lurker, Power User, Confused, Impatient, Mobile, A11y, Distracted, Adversarial.
System Architecture
Autonomous agents orchestrating end-to-end workflows through high-speed MCP bridges.
Real-Time Demo
Sales call triggers transcription, extracts requirements, generates demo, tests it automatically.
Content → CRM
Published content feeds lead tracking. Engagement signals update cohort scores.
QA Loop
Every deploy triggers adversarial testing. Issues feed back into the build pipeline.
Advanced
Adaptive Personalization
Beyond generic RAG. We build systems that learn user preferences and adapt behavior without retraining or prompt bloat.
- ·Segment-specific adapters with routing tool-calls
- ·Continuous extraction of new segments back into traditional ML
- ·Memory stores (mem0, supermemory) for consistent user behavior
- ·Trait-based context injection without bloating prompts
Capabilities
Technical expertise
LLM & Agents
- Claude, GPT, Gemini integration
- MCP protocol orchestration
- Hybrid RAG systems
- DSPy, Outlines for structured output
- RAGAS and custom evaluation
- Guardrails and safety layers
Industrial AI
- Physics-informed neural networks
- CFD/FEA surrogate models
- ANSYS integration
- Digital twin architecture
- RL for control systems
- Sensor fusion pipelines
Infrastructure
- Docker, Kubernetes
- AWS, GCP, Azure
- Modal, Replicate
- Terraform IaC
- ETL pipelines
- Observability & monitoring
Team
Leadership team
Sankalp and Shivam lead a distributed bench of senior engineers, Kaggle Grandmasters, IIT alumni, and industry veterans spanning industrial AI, cloud systems, and applied research.
Services
How we work
Clear pricing. Focused engagements. We ship production systems, not decks.
PWA Sprint
Production-ready Progressive Web App. Built on our source-available boilerplates.
- /Full Source Code Ownership
- /No Licensed Dependencies
- /Offline-first Architecture
- /Docker Deployment
Fractional CTO
Embedded technical leadership. Radical transparency in architecture and hiring.
- /Architecture & Tech Debt Audit
- /Open Strategy Documents
- /Code Review & Standards
- /Hiring Support
- /Investor Technical DD
Agentic Platform
We deploy our proprietary agents into your infra. Full Source Ownership. No IP lock-in.
- /Full MIT License Transfer
- /Sniper/Pulse/Spectre Source
- /Self-Hosted Deployment
- /Zero Vendor Lock-in
- /Training & Handoff
Industrial AI
Open standards for Heavy Industry. Digital Twins and Control Systems.
- /Open PINN Models
- /Transparent Surrogate Models
- /SCADA/PLC Integration
- /Safety Guardrails
FAQ