Deep Tech · Quantum · AI · Biotech
Deep Tech Across Three Frontiers
We advance quantum computing, artificial intelligence, and precision biotech through rigorous research and operational execution. Senior-led from first principles to production deployment.
Delivery stack
- Consulting-firstStrategy, architecture, delivery governance
- Flagship acceleratorsVivik voice · Predicta forecasting
- Research & custom forgeQuantum readiness · bespoke engineering
Target on contact-center pilots
Representative production datasets
Selection · Adaptation · Evolution
Representative benchmarks from pilot deployments. See the research hub or request methodology under NDA. Research hub
Three frontiers
Where we advance the frontier.
Drug discovery, materials science, molecular simulation — the problems that matter most live at the intersection of quantum computing, artificial intelligence, and precision biology.
QUANTUM COMPUTING
Quantum Advantage at Scale
We develop hybrid quantum-classical algorithms that outpace classical search on combinatorial problems, and we harden enterprise cryptographic estates against harvest-now-decrypt-later threats. Research ships as production frameworks, not benchmarks on toy datasets.
Explore quantum workARTIFICIAL INTELLIGENCE
Intelligence for Complex Systems
We build AI that completes tickets, closes forecasting loops, and governs its own compliance posture at inference time. Vivik, Predicta, HelixForge, and Quantum Bridge are production-ready; six research-grade systems cover infrastructure, edge, governance, and interconnect. All lines trace back to published research.
Explore AI workPRECISION BIOTECH
Engineering at the Molecular Scale
We apply our quantum-classical optimisation and AI architecture research to the hardest problems in precision biology: protein structure prediction, molecular simulation at scale, and drug-target identification where classical search fails. HelixForge, our flagship drug discovery platform, delivers ranked candidates from target to lead in 2–4 weeks using graph neural networks, molecular docking, and closed-loop active learning.
Explore biotech workConvergence
Where three disciplines meet.
Drug discovery, materials science, molecular simulation — the problems that matter most live at the intersection. We work there deliberately, not accidentally.
Quantum Meta-Learning
Hybrid architecture search that converges faster than classical AutoML on non-convex objectives — validated in Predicta production pipelines.
HelixForge Drug Discovery
Graph neural networks, molecular docking, and closed-loop active learning deliver ranked drug candidates in 2–4 weeks. 80–90% in vitro confirmation rate vs 30–40% for standard virtual docking.
Molecular Simulation
Quantum-classical hybrid methods reach conformational sampling scales that classical molecular dynamics cannot match efficiently.
Portfolio
Research that ships.
Vivik
Execution-grade conversational AI
Sub-500ms conversational latency with CRM-native action execution. 40% call volume reduction and 95% first-contact resolution in representative deployments. Handles the interactions that used to require manual queues.
View projectPredicta
Predictive intelligence for operational decisions
Operational forecasting for supply chain, treasury, and risk desks. Custom proprietary signal libraries per client industry, scenario modelling for liquidity and credit shocks, real-time alerting into enterprise workflows.
View projectQuantum Bridge
Post-quantum cryptography without waiting for a crisis
Crypto-agile migration tooling aligned to NIST FIPS 203/204/205 and CNSA 2.0. Cryptographic inventory, HNDL risk assessment, and phased migration roadmaps. Defends long-lived secrets against harvest-now-decrypt-later before adversaries catch up.
View projectHyQCOpt
Hybrid quantum-classical optimisation engine
Maps enterprise combinatorial objectives to QUBO and Ising formulations. 25–40% faster convergence vs genetic algorithms on representative benchmark sets. Benchmarked rigorously before any production commitment.
View projectQuantumMetaML
Quantum-accelerated meta-learning
Extends neural architecture search into regimes where classical AutoML exhausts search budgets. Integrated with Predicta pipelines. Published convergence comparisons against classical baselines.
View projectHyperFabric
AI infrastructure fabric
Hardware-software co-design for AI inference at scale. Eliminates data-movement bottlenecks from interconnect through kernel. Material reduction in cross-estate AI latency in representative deployments.
View projectNexus-V
Low-latency AI interconnect
Sub-millisecond-class interconnectivity between distributed AI inference nodes. Built from the same research base as HyperFabric. Reduces coordination overhead in multi-model pipeline architectures.
View projectEdge-Sync
Distributed inference for edge hardware
Pushes model execution to cameras, gateways, and plant-floor devices. Up to 70% reduction in cloud inference costs in representative deployments. Federated updates keep edge models current without bulk retraining.
View projectProtocol-X
Runtime governance for compliant AI deployment
Sub-second regulatory guardrails at inference time. EU AI Act policy packs, audit evidence generation, and real-time constraint enforcement. Governance that ships alongside the model rather than bolted on after.
View projectHelixForge
AI-powered drug discovery — target to lead in 2–4 weeks
Replaces costly wet-lab HTS with an in-silico AI pipeline: graph neural networks, molecular docking, MD simulation, and closed-loop active learning. 80–90% in vitro confirmation rate vs 30–40% for standard virtual docking. Four modalities: target discovery, small molecule, gene therapy, and antibody engineering. $100K–$600K per program.
View projectMolecular Dynamics Research
Quantum-accelerated conformational sampling
Applying hybrid quantum-classical methods to molecular dynamics at scales classical MD cannot reach efficiently. Early-stage research in partnership with computational biology collaborators. Methods are being prepared for publication.
View projectAI-Guided Drug Discovery
Meta-learning applied to target identification
Meta-learning and architecture search applied to drug-target interaction prediction and ADMET profiling. Fewer wet-lab iterations per validated candidate. Active research with select pharmaceutical partners.
View projectComputational Structural Biology
Protein folding and binding affinity prediction
Protein folding ensemble modelling, structure-based virtual screening, and binding affinity prediction using HyperFabric-class infrastructure for throughput. Building toward production-grade computational screening pipelines.
View projectResearch-driven architecture
Frontier research translates to production advantage.
We publish on quantum-classical optimization, post-quantum security, and AI infrastructure, then ship the same rigor into flagship products and client deployments.
Quantum computing & cryptographic readiness
Post-quantum security
NIST PQC standards (FIPS 203/204/205), harvest-now-decrypt-later risk, and CNSA 2.0 migration planning, delivered through Quantum Bridge and assessment-led consulting.
- Cryptographic inventory & HNDL risk assessment
- PQC migration roadmaps aligned to CNSA 2.0
- Quantum Bridge orchestration for phased cutovers
Google Scholar · AI systems & biosciences
26+ lab research publications
Krishna Bajpai publishes on quantitative finance, edge AI, and swarm optimization. Dr. Mamta Shukla contributes 17+ peer-reviewed biosciences papers with 353+ citations, together 355+ citations across the lab.
- Krishna: preprints, SSRN & working papers on AI systems
- Dr. Mamta: phytochemistry, pharmacology & biosciences
- Open-source components across ML and infrastructure
HyperFabric · Nexus-V · Protocol-X
AI infrastructure & optimization
HyperFabric interconnect research, sub-millisecond-class data movement, and Protocol-X governance for EU AI Act and cryptographic compliance at inference time.
- HyperFabric: data-movement bottleneck elimination
- HyQCOpt hybrid quantum-classical search
- Protocol-X: runtime policy intercept & audit trails
What brings you here?
Choose your starting point.
Operations, research, or security, three paths into the lab.
Operations leaders
Reduce AI cost & execution risk
Flagship products, Lab Engagement Model, and production cutovers for hospitality, healthcare, logistics, and shared services.
View flagship productsCTOs & innovation
Research-driven architecture
Hybrid AI, quantum optimization, federated learning, and benchmarks, published research translated into production systems.
Explore researchQuantum & security
Post-quantum readiness
Cryptographic assessments, PQC migration roadmaps, and Protocol-X governance for CNSA 2.0 and EU AI Act timelines.
Quantum readinessFlagship products
Built to ship, not to pilot.
Vivik, Predicta, HelixForge, and Quantum Bridge are our production-ready flagships — typical pilot timeline 2–4 weeks, production cutover 4–8 weeks.
Vivik
Execution-grade conversational AI
40% call volume reduction
Human-grade conversational interfaces with sub-500ms latency that take real actions in your systems
Visit product sitePredicta
Predictive models that see through the noise
85% forecast accuracy
Predictive models that see through the noise - specialized for high-frequency data, supply chain volatility, and financial risk
Visit product siteResearch-grade systems delivered through scoped deep tech engagements, structured timelines and integration via the Lab Engagement Model.
View all flagship productsBoutique consulting
Senior-led work when theSKU doesn't exist yet.
We are a consultancy first. Flagship products accelerate proven paths; these three practices cover extension, architecture, and net-new builds, each owned by the same senior team that ships production systems.
View consulting services- 01
Product-Accelerated Model
Deploy in weeks, not monthsWeeks-to-production on flagship cores you already trust in the field.
See capabilities - 02
Architectural Advisory
The 'Brain' for your infrastructureExecutive alignment, compliance posture, and integration blueprints before build burn.
See capabilities - 03
Custom Forge
Build a product for your nicheNet-new engineering when the catalog stops short, same production bar, your niche.
See capabilities
Our approach
Rigorous method across all three frontiers.
Our methodology spans quantum, AI, and biotech. Whether advancing quantum error correction, building neural architectures for complex systems, or engineering at the molecular scale — we combine scientific rigour with execution-grade delivery.
Boutique execution
Senior ownership. One accountable path from strategy to production.
Fragmented vendors
Multiple agencies, shelfware handoffs, and roadmaps that never reach production.
Selection
Anchor on the right flagship foundation before build burn.
Adaptation
Re-architect for your stack, SLAs, and compliance reality.
Evolution
Research-as-a-service so deployments stay current after go-live.
The Lab Engagement Model ties selection, adaptation, and evolution into one delivery path. Senior ownership from scoping through cutover.
Explore our approachHow we work
Strategy-led delivery.Products when they fit; custom when they must.
Bajpai Labs is a boutique practice: senior attention, tight scopes, and ownership of the stack we ship, from discovery through production.
Executive alignment
We pressure-test the real constraints: volume, compliance, systems, and what “good” looks like on paper versus in production.
Architecture & plan
You get a delivery blueprint, flagship product fit, custom integration, timelines, and exit criteria, not a generic AI roadmap.
Pilot to scale
Small surface area first, measurable outcomes, then harden for scale. We stay engaged until the metric moves, not until the deck ships.
Flagship products
When your problem matches something we already hardened in production, we deploy Vivik or Predicta with minimal invention risk. When it does not, we design and build with the same engineering bar.
Strategic session
Start with a pressure-test, not a slide deck.
A 60-minute working session: we map where AI strategy, flagship products, or a custom build will move the needle fastest, and what to ignore. Most teams start with a diagnostic before a full engagement.