Why Confidential Computing Must Evolve Beyond Isolated Workloads

Hardware enclaves like Intel SGX and AMD SEV are a good start, but they create a false sense of security. Modern AI workloads involve complex data pipelines where sensitive information is exposed outside the enclave during pre-processing, vectorization, and inference orchestration. This article argues that true data protection requires a shift from isolated confidential workloads to end-to-end confidential pipelines, integrating policy-aware connectors, PII redaction as code, and centralized AI security platforms.

THE ARCHITECTURAL FLAW

The False Promise of the Isolated Enclave

Hardware-based trusted execution environments (TEEs) create a false sense of security by protecting only the AI model's runtime, not the end-to-end data pipeline.

Isolated enclaves fail because they protect only the AI model's runtime, leaving sensitive data exposed during pre-processing, feature extraction, and post-inference aggregation. A confidential computing strategy that stops at the enclave boundary is architecturally incomplete.

The attack surface shifts from the model to the data pipeline. Raw customer data must be decrypted and transformed before entering the secure enclave, creating a vulnerable data marshaling layer that attackers target. Frameworks like TensorFlow or PyTorch running outside the TEE become the new perimeter.

Real-world evidence shows this gap. A 2023 academic study demonstrated that side-channel attacks could infer training data from a model running inside an Intel SGX enclave by analyzing memory access patterns during inference. The enclave was secure; the data pipeline was not.

The solution is end-to-end confidential pipelines. This requires integrating policy-aware connectors that enforce data residency and redaction before ingestion, and using software-based runtime encryption for data-in-transit between pipeline stages. Learn more about building these pipelines in our guide on end-to-end confidential AI.

Compare the approaches. An isolated enclave is a fortified castle with an unguarded supply line. An end-to-end confidential pipeline is a continuously escorted convoy, where data remains protected from source to sink. The latter is the only viable architecture for governed AI.

BEYOND SILOED ENCLAVES

Key Trends Driving the Need for End-to-End Confidential Pipelines

Isolated hardware enclaves are insufficient for modern AI; these three trends mandate protection across the entire data pipeline.

The Data Sovereignty Imperative

Global regulations like the EU AI Act and GDPR impose strict data residency and processing requirements. Isolated confidential workloads fail when data moves between pre-processing, training, and inference stages across hybrid clouds.

Jurisdictional Risk: Processing data in the wrong region can trigger fines of up to 4% of global revenue.
Policy Enforcement: Requires policy-aware connectors that enforce geo-fencing and redaction before data enters any compute environment.

GDPR Fine Risk

70+

Global Data Laws

The Third-Party API Blind Spot

Modern AI stacks rely on external APIs from providers like OpenAI, Anthropic Claude, and Google Gemini. Data sent to these services exits your controlled enclave, creating an ungoverned exfiltration vector.

Unmanaged Risk: Most AI security platforms lack visibility into data transformations within third-party black-box models.
Solution: A centralized PET dashboard and gateway that encrypts, logs, and redacts all data in transit to external AI services.

100%

Data Exposure

Native Visibility

The AI Supply Chain Attack Surface

Model inversion and membership inference attacks can reconstruct sensitive training data from a deployed model. Protecting only the inference runtime ignores the upstream training pipeline where the original data breach occurs.

Data Lineage Nightmare: Without PET-instrumented lineage tracking, you cannot audit where PII flowed, creating massive compliance liabilities.
Solution: End-to-end confidential pipelines that apply differential privacy and secure multi-party computation during data aggregation and model training.

10k+

Training Samples Reconstructed

$3.9M

Avg. Breach Cost

The Hybrid Cloud Data Gravity Problem

Sensitive 'crown jewel' data often resides on-premises, while scalable LLM training happens in the public cloud. Moving encrypted data for processing in a remote TEE introduces latency and leaves data in transit vulnerable.

Inference Economics: The cost of moving petabytes to cloud enclaves is prohibitive.
Solution: Federated confidential computing that extends Trusted Execution Environments (TEEs) across hybrid infrastructure, enabling local processing with centralized policy control.

~500ms

Added Latency

40%

Data Transfer Cost

The PII Redaction Scaling Crisis

Manual or rule-based redaction cannot scale with the volume and variety of unstructured data ingested for AI. Static rules destroy context, ruining model utility, while missed PII creates legal risk.

Agility Block: Manual processes break CI/CD pipelines and slow AI iteration.
Solution: PII redaction 'as code'—treating anonymization as a version-controlled, immutable pipeline component that uses NLP for context-aware detection.

-90%

Manual Effort

99.5%

Accuracy

The Software Guard Necessity

Hardware TEEs like Intel SGX and AMD SEV have known vulnerabilities and implementation complexities. Relying solely on hardware creates a single point of failure in the trust model.

Defense-in-Depth Gap: A compromised enclave can lead to total data exposure.
Solution: A hybrid trusted execution model that layers application-level runtime encryption, software guards, and remote attestation on top of hardware roots of trust.

20+

TEE CVEs

10x

Architecture Complexity

THE PIPELINE PROBLEM

Where Isolated Confidential Computing Breaks Down

Hardware enclaves fail to protect data during pre-processing, model inference, and output generation, creating critical security gaps.

Isolated enclaves are insufficient because modern AI workloads are end-to-end data pipelines, not single functions. A Trusted Execution Environment (TEE) secures data only while inside its specific CPU core, but data is exposed when moved for tasks like embedding generation in Pinecone or Weaviate or inference with a model served by vLLM. This creates a 'confidential computing gap' where protection is lost between secured stages.

Pre-processing is the weakest link. Data must be decrypted for tokenization, vectorization, and cleaning before entering an enclave. This unencrypted pre-processing phase is a prime target for memory scraping attacks and undermines the entire privacy promise. Techniques like policy-aware connectors are required to enforce redaction before data touches any compute resource.

Modern AI stacks are polyglot. A single request may flow through an OpenAI API, a custom fine-tuned model, and a LangChain orchestration layer. An isolated TEE cannot span these distributed services. Security becomes a lowest common denominator issue, where one vulnerable component compromises the whole chain, a core challenge addressed in AI TRiSM.

Evidence: Studies show over 60% of data exposure in AI systems occurs during the pre-processing and post-processing phases outside of TEEs. Relying solely on hardware isolation is like installing a vault door in a house made of glass.

DATA-IN-USE EXPOSURE POINTS

Vulnerability Matrix: The AI Data Pipeline

This matrix compares the data protection posture of three common AI pipeline architectures, highlighting where sensitive data is exposed during processing.

Pipeline Stage / Attack Vector	Traditional Cloud Pipeline	Isolated Confidential Enclave	End-to-End Confidential Pipeline
Data Ingestion & Pre-processing	Raw data decrypted in memory; accessible to cloud OS/hypervisor.	Raw data decrypted inside enclave; protected from host OS.	Data remains encrypted or tokenized; pre-processing occurs within TEE.
Model Inference / Training	Model weights and activations in plaintext in GPU/CPU memory.	Model execution inside enclave; memory encrypted via CPU SEV/SGX.	Entire computational graph (embeddings, attention) runs in encrypted domain.
Intermediate Data (e.g., Embeddings, Logits)	Stored in plaintext in vector DB (e.g., Pinecone, Weaviate) memory.	Temporarily exposed when moved between enclaves for distributed tasks.	Persistently encrypted or operated on via Homomorphic Encryption or Secure Multi-Party Computation.
Model Output / Post-processing	Results decrypted; potential for PII reconstruction via inversion attacks.	Results decrypted inside enclave before leaving; limited protection.	Results remain encrypted or are de-identified via differential privacy before export.
Adversarial Attack Resistance (e.g., Model Inversion)
Compliance with Data Residency (e.g., EU AI Act)	High risk of violation due to uncontrolled data flow.	Moderate risk; depends on enclave physical location attestation.	Enforced by policy-aware connectors at ingestion; geo-fencing inherent.
Required Architectural Overhaul	None (baseline)	Medium: Isolate sensitive workloads into TEEs (e.g., Azure Confidential VMs).	High: Integrate PET frameworks (e.g., OpenMined) and hybrid TEEs.
Typical Latency Overhead vs. Baseline	0%	5-15%	20-40% (for advanced PETs like HE)

THE ARCHITECTURAL SHIFT

Blueprint for End-to-End Confidential AI Pipelines

Isolated hardware enclaves fail to protect data across the entire AI lifecycle, demanding a pipeline-wide confidential architecture.

Confidential computing must evolve beyond protecting isolated workloads to securing entire data pipelines. Hardware-based Trusted Execution Environments (TEEs) like Intel SGX or AMD SEV create secure enclaves, but data remains vulnerable during pre-processing, feature extraction, and inference orchestration outside these silos.

The attack surface expands with each pipeline stage. Data decrypted for embedding by a model like OpenAI's text-embedding-3-large or indexing in a vector database such as Pinecone or Weaviate creates leakage points. An end-to-end approach encrypts data throughout its entire computational journey.

Isolated TEEs versus pipeline encryption represents a critical architectural choice. A TEE protects a fine-tuning job within an Azure Confidential VM, but a pipeline approach also secures data flowing through Apache Spark for ETL and vLLM for inference. The latter prevents exfiltration at hand-off points.

Evidence: Research shows over 60% of data exposure in AI systems occurs during pre-processing and model serving, not within the core training workload. This mandates protection that spans from data connectors to the final inference API, a principle central to our work on AI TRiSM.

BEYOND ISOLATED ENCLAVES

Essential Components of a Confidential Pipeline

Protecting data-in-use requires end-to-end confidential pipelines, not just isolated enclaves, to prevent leaks during pre-processing and inference.

The Problem: Data Leakage in Pre-Processing

Hardware Trusted Execution Environments (TEEs) like Intel SGX or AMD SEV only protect the core computation. Sensitive data is exposed during ingestion, transformation, and vectorization before it ever reaches the secure enclave. This creates a massive attack surface in the data pipeline.

Attack Vector: Unencrypted data in memory during tokenization or embedding.
Compliance Gap: Violates data residency and privacy-by-design principles.
Real Consequence: A model trained on PII can leak it via inversion attacks.

~70%

Pipeline Exposed

GDPR

Non-Compliant

The Solution: Policy-Aware Connectors

Intelligent data connectors act as the first line of defense, enforcing privacy policies at the point of ingestion. They redact PII, apply geo-fencing, and tag data with usage constraints before it enters any AI workflow.

Key Benefit: Automated PII redaction 'as code' ensures consistency and auditability.
Key Benefit: Prevents policy violations by blocking non-compliant data flows to third-party APIs like OpenAI or Anthropic Claude.
Integration: Essential for compliance with the EU AI Act and other evolving regulations.

-99%

PII Exposure

Real-Time

Policy Enforcement

The Problem: Blind Spots in Third-Party AI

Most AI security platforms cannot govern data once it leaves your infrastructure for external model APIs. This creates unmanaged risk and a complete lack of visibility into how sensitive data is processed by vendors.

Blind Spot: No control over data retention or secondary use by API providers.
Governance Paradox: Planning for agentic AI without the tools to oversee it.
Vendor Lock-in Risk: Inability to switch providers due to opaque data handling.

100%

External Risk

$10M+

Potential Fines

The Solution: Centralized PET Dashboard

A unified AI security platform provides cross-application visibility and control. It instruments data lineage across hybrid clouds and third-party services, applying PETs consistently regardless of where computation occurs.

Key Benefit: Centralized governance for models from OpenAI, Google Gemini, and Hugging Face.
Key Benefit: PET-instrumented lineage tracking proves data provenance for audits.
Foundation: Enables a true zero-trust data processing model for AI.

360°

Visibility

1 Platform

Unified Control

The Problem: Incompatible Legacy Encryption

Standard encryption tools break vector databases and embedding models. Legacy encryption is AI-opaque, rendering data unusable for similarity search or model training, forcing teams to choose between utility and privacy.

Performance Hit: Homomorphic encryption can introduce ~1000x latency for inference.
Architectural Debt: Bolt-on PETs create complexity and gaps in the MLOps lifecycle.
Stalled Adoption: Makes PET seem impractical for real-time production AI.

1000x

Slower Inference

Bolt-On

High Overhead

The Solution: AI-Native PET Frameworks

Next-generation frameworks provide runtime encryption compatible with the AI stack. They protect data during computation within TEEs and during transit between pipeline stages, using techniques like secure multi-party computation (SMPC) for collaborative training.

Key Benefit: Enables confidential federated learning and RAG across organizational boundaries.
Key Benefit: Integrates directly with ModelOps tools like Weights & Biases and vLLM for secure deployment.
Strategic Imperative: Turns PET from a compliance cost into an enabler for secure data collaboration.

<500ms

Latency Added

E2E

Confidential Pipeline

THE REALITY

The Performance Overhead Objection (And Why It's Wrong)

The perceived latency and cost penalties of confidential computing are based on outdated benchmarks of isolated workloads, not modern, optimized AI pipelines.

The performance overhead objection is obsolete. Modern Trusted Execution Environments (TEEs) like Intel SGX and AMD SEV impose single-digit percentage latency for AI inference, a negligible cost compared to the risk of a data breach or non-compliance fine under regulations like the EU AI Act.

Isolated enclave benchmarks are misleading. Early tests measured the cost of protecting a single, siloed process. Real-world confidential AI uses orchestrated, end-to-end pipelines where encryption and attestation are continuous, not episodic. Frameworks like TensorFlow Privacy and PySyft integrate privacy-enhancing technologies (PETs) directly into the data flow, amortizing overhead across the entire system.

The comparison is flawed. The debate should not be confidential vs. non-confidential compute. The correct comparison is the cost of overhead versus the cost of a breach. For industries like healthcare and finance, where a single PII leak incurs multi-million dollar penalties, a 5% latency increase is a strategic investment, not a tax. This is the core principle behind building a PET-first architecture.

Evidence from production systems. Microsoft's Azure Confidential Computing platform reports that confidential inference with ONNX Runtime sees less than 10% throughput degradation for models like BERT. In high-stakes scenarios, this minor trade-off enables previously impossible data collaborations, such as federated learning on sensitive genomic datasets, which would be non-starters without PETs.

THE END-TO-END IMPERATIVE

Key Takeaways

Isolated hardware enclaves are insufficient for modern AI; true data protection requires confidential computing to evolve into a holistic, architectural principle.

The Problem: The Data Transit Gap

Hardware Trusted Execution Environments (TEEs) protect data only within the CPU. Pre-processing, feature extraction, and data loading often occur outside the enclave, creating critical exposure points. A single unencrypted data transfer can nullify the entire security model.

Attack Surface: Data is vulnerable during serialization/deserialization and I/O operations.
Compliance Risk: Violates the principle of 'data protection by design' mandated by regulations like GDPR and the EU AI Act.
Real-World Impact: A model trained on encrypted health records can still leak PII if the data pipeline isn't fully instrumented.

~70%

Of Pipeline Exposed

High

Compliance Risk

The Solution: End-to-End Confidential Pipelines

Extend the trust boundary from isolated workloads to the entire AI data lifecycle. This requires integrating policy-aware connectors, in-memory encryption, and secure multi-party computation to create a continuous chain of custody.

Architectural Shift: Treat PET as a foundational layer, not a bolt-on. This aligns with our pillar on AI TRiSM for holistic governance.
Key Enabler: Use confidential containers and runtime encryption to protect data during all phases of movement and computation.
Business Outcome: Enables safe processing of sensitive data across hybrid clouds and with third-party models, unlocking use cases in Precision Medicine and Fintech.

End-to-End

Chain of Custody

Zero-Trust

Data Processing

The Problem: Siloed PET Creates Governance Blind Spots

Using point solutions for encryption, redaction, and access control in isolation creates unmanaged risk. You cannot govern what you cannot see, especially when data flows to external APIs from OpenAI, Anthropic Claude, or Hugging Face.

Operational Overhead: Managing multiple, disconnected PET tools increases complexity and cost.
Critical Gap: Lack of centralized visibility into how sensitive data is transformed within black-box AI models.
Strategic Liability: Makes demonstrating compliance for audits under frameworks like AI TRiSM nearly impossible.

High

Management Cost

Unmanaged

Third-Party Risk

The Solution: Centralized AI Security Platforms

Deploy a unified control plane that provides cross-application visibility and policy enforcement across all AI workloads and third-party integrations. This is the core of mature AI TRiSM.

Core Function: Centralize logging, monitoring, and policy execution for every data interaction with internal and external models.
Integration Point: These platforms must natively integrate with MLOps tools like Weights & Biases and secure deployment stacks like vLLM.
Strategic Benefit: Transforms PET from a technical checkbox into a business enabler for Sovereign AI deployments and secure Multi-Modal Enterprise Ecosystems.

360°

Visibility

Automated

Policy Enforcement

The Problem: Static Redaction Destroys Data Utility

Traditional, rule-based PII redaction is brittle. It either misses contextually sensitive information or over-redacts, stripping the data of value needed for accurate AI training and inference. This cripples RAG systems and Knowledge Engineering initiatives.

Accuracy Trade-off: Simple regex fails on unstructured text, leading to data leaks or useless training sets.
Development Friction: Manual review and rule updates cannot keep pace with agile AI-Native SDLC practices.
Business Cost: Reduces model performance and increases the time-to-insight, directly impacting ROI.

Low

Accuracy

High

Manual Effort

The Solution: PII Redaction 'As Code'

Treat data anonymization as an immutable, version-controlled pipeline component. Implement context-aware redaction engines that use NLP to understand semantic meaning, ensuring precise protection without destroying analytical utility.

Technical Foundation: Codify redaction rules into CI/CD pipelines, enabling automated, auditable, and consistent privacy protection.
Advanced Capability: Leverage models trained to identify not just explicit PII but also quasi-identifiers and sensitive context.
Strategic Alignment: This approach is non-negotiable for agile teams and is a prerequisite for Continuous PET Validation, a core concept for future AI compliance. It directly supports secure Legacy System Modernization by safely mobilizing dark data.

As Code

Automated & Auditable

Context-Aware

High Fidelity

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THE ARCHITECTURE SHIFT

Stop Building Vaults, Start Building Fortresses

Isolated hardware enclaves are insufficient; modern AI demands end-to-end confidential pipelines that protect data during every stage of processing.

Confidential computing is not a vault for isolated workloads; it is the architectural blueprint for a data fortress. Hardware-based Trusted Execution Environments (TEEs) like Intel SGX or AMD SEV create secure enclaves, but they protect only the computation inside them. Modern AI workflows—involving data ingestion, preprocessing in Pandas or Apache Spark, vectorization for Pinecone or Weaviate, and inference via APIs—create multiple points of data exposure outside the enclave. An isolated vault leaves the connecting corridors unguarded.

The attack surface expands with every integration. A model secured in an enclave is useless if sensitive customer data is exposed during pre-processing in an unsecured Kubernetes pod or when query results are sent to an external OpenAI or Anthropic Claude API. The defense-in-depth principle requires that encryption and access controls persist as data moves between systems. This is why a fortress, with layered walls and guarded gates, is the necessary evolution from a single strongroom.

Evidence from real breaches confirms this. Research demonstrates that model inversion attacks can reconstruct training data from model outputs, turning your fine-tuned LLM into a data leak. Furthermore, without end-to-end lineage tracking—a core component of AI TRiSM—you cannot audit where PII flowed after it left the initial enclave, creating massive compliance liabilities under regulations like the GDPR and EU AI Act.

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.

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Why Confidential Computing Must Evolve Beyond Isolated Workloads

Pipeline Stage / Attack Vector

Traditional Cloud Pipeline

Isolated Confidential Enclave

End-to-End Confidential Pipeline

Data Ingestion & Pre-processing

Raw data decrypted in memory; accessible to cloud OS/hypervisor.

Raw data decrypted inside enclave; protected from host OS.

Data remains encrypted or tokenized; pre-processing occurs within TEE.

Model Inference / Training

Model weights and activations in plaintext in GPU/CPU memory.

Model execution inside enclave; memory encrypted via CPU SEV/SGX.

Entire computational graph (embeddings, attention) runs in encrypted domain.

Intermediate Data (e.g., Embeddings, Logits)

Stored in plaintext in vector DB (e.g., Pinecone, Weaviate) memory.

Temporarily exposed when moved between enclaves for distributed tasks.

Persistently encrypted or operated on via Homomorphic Encryption or Secure Multi-Party Computation.

Model Output / Post-processing

Results decrypted; potential for PII reconstruction via inversion attacks.

Results decrypted inside enclave before leaving; limited protection.

Results remain encrypted or are de-identified via differential privacy before export.

Adversarial Attack Resistance (e.g., Model Inversion)

Compliance with Data Residency (e.g., EU AI Act)

High risk of violation due to uncontrolled data flow.

Moderate risk; depends on enclave physical location attestation.

Enforced by policy-aware connectors at ingestion; geo-fencing inherent.

Required Architectural Overhaul

None (baseline)

Medium: Isolate sensitive workloads into TEEs (e.g., Azure Confidential VMs).

High: Integrate PET frameworks (e.g., OpenMined) and hybrid TEEs.

Typical Latency Overhead vs. Baseline

5-15%

20-40% (for advanced PETs like HE)

Technical Foundation: Codify redaction rules into CI/CD pipelines, enabling automated, auditable, and consistent privacy protection.
Advanced Capability: Leverage models trained to identify not just explicit PII but also quasi-identifiers and sensitive context.
Strategic Alignment: This approach is non-negotiable for agile teams and is a prerequisite for Continuous PET Validation, a core concept for future AI compliance. It directly supports secure Legacy System Modernization by safely mobilizing dark data.

Why Confidential Computing Must Evolve Beyond Isolated Workloads

The False Promise of the Isolated Enclave

Key Trends Driving the Need for End-to-End Confidential Pipelines

The Data Sovereignty Imperative

The Third-Party API Blind Spot

The AI Supply Chain Attack Surface

The Hybrid Cloud Data Gravity Problem

The PII Redaction Scaling Crisis

The Software Guard Necessity

Where Isolated Confidential Computing Breaks Down

Vulnerability Matrix: The AI Data Pipeline

Blueprint for End-to-End Confidential AI Pipelines

Essential Components of a Confidential Pipeline

The Problem: Data Leakage in Pre-Processing

The Solution: Policy-Aware Connectors

The Problem: Blind Spots in Third-Party AI

The Solution: Centralized PET Dashboard

The Problem: Incompatible Legacy Encryption

The Solution: AI-Native PET Frameworks

The Performance Overhead Objection (And Why It's Wrong)

Key Takeaways

The Problem: The Data Transit Gap

The Solution: End-to-End Confidential Pipelines

The Problem: Siloed PET Creates Governance Blind Spots

The Solution: Centralized AI Security Platforms

The Problem: Static Redaction Destroys Data Utility

The Solution: PII Redaction 'As Code'

Build AI Search, AI Agents, and Product AI

Search across company data

Automate internal workflows

Add AI to products and internal tools

Stop Building Vaults, Start Building Fortresses

Prasad Kumkar

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Why Confidential Computing Must Evolve Beyond Isolated Workloads

The False Promise of the Isolated Enclave

Key Trends Driving the Need for End-to-End Confidential Pipelines

The Data Sovereignty Imperative

The Third-Party API Blind Spot

The AI Supply Chain Attack Surface

The Hybrid Cloud Data Gravity Problem

The PII Redaction Scaling Crisis

The Software Guard Necessity

Where Isolated Confidential Computing Breaks Down

Vulnerability Matrix: The AI Data Pipeline

Blueprint for End-to-End Confidential AI Pipelines

Essential Components of a Confidential Pipeline

The Problem: Data Leakage in Pre-Processing

The Solution: Policy-Aware Connectors

The Problem: Blind Spots in Third-Party AI

The Solution: Centralized PET Dashboard

The Problem: Incompatible Legacy Encryption

The Solution: AI-Native PET Frameworks

The Performance Overhead Objection (And Why It's Wrong)

Key Takeaways

The Problem: The Data Transit Gap

The Solution: End-to-End Confidential Pipelines

The Problem: Siloed PET Creates Governance Blind Spots

The Solution: Centralized AI Security Platforms

The Problem: Static Redaction Destroys Data Utility

The Solution: PII Redaction 'As Code'

Build AI Search, AI Agents, and Product AI

Search across company data

Automate internal workflows

Add AI to products and internal tools

Stop Building Vaults, Start Building Fortresses

Prasad Kumkar

We work with leading teams building AI, Software and Data.

Tell us what you want AI to do.

Review the use case

Pick the right approach

Build the first useful version

Improve from there