DataSum

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AI Acceleration

Innovate. Accelerate. Scale.

Overview

What We Build

DataSum delivers a unique engineering-led approach to AI/ML adoption—one that prioritizes pragmatic integration over radical reinvention. We build a spectrum of intelligent systems that include:

  • Predictive analytics models for forecasting, scoring, and anomaly detection
  • Enterprise-grade LLM copilots integrated into developer tools, internal platforms, and ops workflows
  • Orchestration agents that automate deployment, ticket triage, document summarization, and infra ops
  • AI-enhanced test automation that generates test cases, auto-validates outputs, and reduces QA cycles

These aren’t PoCs or tech showcases—they are deeply embedded, production-grade systems.

How We Deliver

We integrate intelligence directly into your technology backbone—whether that’s Kubernetes clusters, CI/CD pipelines, APIs, or microservices. Our engineers align AI/ML capabilities with your dev team’s velocity, ops team’s governance needs, and product team’s UX goals.
Whether you’re scaling a GenAI MVP or transforming a legacy workflow with machine intelligence, DataSum helps you accelerate AI adoption without disrupting your stack, budget, or compliance model.

We don’t offer isolated experiments—we build complete systems that support real-time inference, automate workflows, scale across environments, and comply with your governance requirements. Whether you need a recommendation engine, domain-specific copilots, or autonomous infra-monitoring agents, DataSum ensures these capabilities are engineered—not bolted on.

Our Core differentiator? 

We turn AI from an R&D function into a production-grade asset—secure, scalable, and sprint-ready. to AI/ML adoption—one that prioritizes pragmatic integration over radical reinvention. Enterprises often face a paradox: they want the benefits of GenAI and ML, but without disrupting their infrastructure, processes, or compliance models. That’s where DataSum stands apart.
We embed intelligence into your existing digital fabric, aligning with your technology stack, developer workflows, and business logic. From ML pipelines to intelligent agents and GenAI copilots, DataSum turns AI aspirations into production-grade systems with tangible outcomes.
Our core differentiator? We don’t build academic prototypes. We build secure, scalable, and governed AI that delivers value from sprint one.

Core Differentiation Pillars

Engineering-Led AI: Where Full-Stack Meets Full-Scope

DataSum’s DNA lies in building robust, scalable, and cloud-native digital platforms—AI is simply an extension of that engineering philosophy. We don’t treat AI/ML as separate innovation experiments. Instead, we embed it deeply into the product development lifecycle—touching everything from dev pipelines to runtime environments. This ensures seamless alignment between data science teams and engineering squads, allowing us to ship intelligent features rapidly and reliably.

Highlights & Sub-Offerings:
Embedded AI into DevOps Toolchains: We inject ML model scoring, drift detection, and retraining triggers directly into CI/CD workflows using Jenkins, GitHub Actions, and ArgoCD. This enables automated model governance, rollback, and testing as part of every release.
Smart Observability & AIOps: Our engineers incorporate AI models into Prometheus/Grafana dashboards to predict anomalies, detect incident root causes, and recommend remediations—turning operations teams into proactive responders.
Test Automation with AI: By integrating LLMs and ML-based test case generation tools into your QA pipelines, we auto-generate unit, integration, and edge-case tests, improving coverage and reducing manual effort.
Data-Driven Microservices: We architect ML-ready APIs and microservices that serve real-time model predictions using Ray Serve, TorchServe, or Triton. These APIs are containerized, scalable, and built to plug into any cloud-native environment.
Cross-functional Agile Pods: Each DataSum pod includes a mix of full-stack developers, MLOps engineers, and data scientists. These embedded teams co-develop features with model-powered intelligence—eliminating siloed experimentation.
Outcome:

AI features become native to your product lifecycle. No more isolated PoCs or disconnected ML models—just fast, iterative, and production-grade intelligence that works with your tech stack, not against it.

Faster iterations, fewer handoffs, and AI that fits your platform—not the other way around.

Built on the Best: Open Toolchains, Modular Architectures

DataSum’s value lies in its ability to cherry-pick the most effective, battle-tested components across the AI/ML landscape—and assemble them into scalable, modular, and performance-driven systems. We focus on open-weight models, best-in-class libraries, and container-native runtimes that ensure zero vendor lock-in while supporting long-term innovation.

Stack Integration Expertise:
ML Frameworks & Libraries: We apply the right ML tool for the job—whether it’s TensorFlow for image-based models, PyTorch for NLP, or XGBoost for tabular data. Our model pipelines are structured for portability, tuning, and reproducibility.
AI/ML Operations: Using MLflow, DVC, and custom metadata tracking, we version every model artifact and experiment. Our MLOps architecture supports lifecycle automation, reproducibility, and CI/CD integration.
Enterprise-Ready LLMs: We specialize in integrating enterprise-grade LLMs such as OpenAI, Meta LLaMA, Codestral, and others into proprietary environments using prompt chaining, context optimization, and RAG flows.
Agents and Planners: We configure LangGraph and CrewAI-based multi-agent systems to work with your enterprise tools—routing tasks, handling failures, and maintaining task state across interactions.
Data & Vector Infrastructure: Our teams architect pipelines with Delta Lake, Kafka, Airbyte, and dbt to feed downstream vector DBs like Pinecone or RedisVector—supporting high-performance semantic search and retrieval.
Outcome:

Composability, performance, and transparency—delivered through open yet enterprise-hardened architectures that future-proof your AI investments.

Modular systems that scale. No lock-in. Full transparency. Full performance.

Agentic AI Systems: Beyond Prompting, Into Autonomy

At DataSum, we design and deploy enterprise-grade autonomous agents that operate beyond scripted interactions. These systems are powered by modular LLM architectures, robust memory, and dynamic tool integration—enabling them to function as decision-makers, orchestrators, and task executors within complex enterprise environments.

Agent Capabilities & Architecture:
Autonomous Decision Loops: Our agents use planning frameworks like LangGraph to evaluate input, assess state, and decide next actions using reasoning chains and retrievable memory.
Specialist Multi-Agent Ecosystems: We implement CrewAI-like frameworks where multiple agents—such as a classifier, planner, executor, and QA checker—collaborate asynchronously to fulfill high-value workflows like incident response, code generation, or ticket routing.
LLM-Integrated API Orchestration: Agents are capable of consuming structured APIs, invoking cloud services, and chaining outputs across endpoints to drive real-world actions—not just responses.
Persistent Context Management: Vector-based long-term memory modules store past interactions, user preferences, and state transitions, enabling high-context, multi-session continuity.
Monitoring & Fall-back: All agent operations are governed via circuit breakers, audit logs, and escalation paths to human operators—ensuring safe autonomy.
Outcome:

Agents that do more than talk—they understand, act, and adapt across evolving enterprise contexts. Actionable autonomy—not marketing demos. DataSum agents think, act, and improve over time.

Agents that do real work: summarize, route, code, escalate, test, orchestrate.

AI DevOps: Shipping AI Like Software

AI isn’t mature until it’s monitored, reproducible, and version-controlled. DataSum brings proven DevOps principles to AI/ML engineering—so models, pipelines, and data behave like software.

DevOps-Driven AI Engineering:
Model Lifecycle as Code: Our teams define training, evaluation, and deployment workflows as declarative YAML specs or pipelines—using GitOps principles for traceability and rollback.
Multi-Environment Support: We support staging, canary, and shadow modes across production inference clusters—de-risking updates to AI behavior before live rollout.
Runtime Monitoring & Alerts: Our engineers integrate Prometheus, Grafana, and custom probes to monitor model latency, throughput, and output variance—automatically triggering retraining or rollback.
Security and Artifact Integrity: Models are signed, hashed, and pushed via secure registries. We enforce linting, dependency checks, and reproducibility as part of every CI/CD job.
Outcome:

AI that ships like code—secure, repeatable, observable, and rollback-safe.

No more AI silos. Just product-grade pipelines that treat models like code.

Custom Copilots, Private LLMs, and RAG Systems

DataSum builds enterprise copilots that integrate tightly with internal knowledge bases, systems, and processes. These are not plug-and-play chatbot UIs—they’re intelligent work companions developed on open-weight LLMs, configured with security layers, and tailored to your domain.

Copilot Engineering Capabilities:
Contextual Prompt Routing: We build prompt routing and templating logic using prompt hubs and type-safe interfaces that abstract API complexity from end users.
Hybrid LLM Deployment: Copilots can run via hosted APIs (OpenAI, Anthropic) or be containerized in VPCs using OSS models (LLaMA, Mistral) with GPU auto-scaling and telemetry.
Grounded Retrieval Flows (RAG): We construct RAG pipelines with enterprise-specific document loaders, vector stores (like Pinecone/Qdrant), and embedding models. These are fine-tuned to filter, chunk, and cite content dynamically.
Tool-Aware Actions: Copilots can call backend systems, execute workflows (e.g., Jira updates, test runs), and surface insights based on role and permissions.
Security & Observability: Every user interaction is governed by RBAC, logged via audit hooks, and evaluated via safety classifiers to avoid hallucinations and non-compliance.
Outcome:

Intelligent copilots engineered for real work—not marketing demos. Secure, scalable, and tuned to your unique environment. Copilots you can trust—with the security, intelligence, and UX your enterprise demands.

Control your AI. Protect your IP. Ensure your copilots speak your domain fluently.

Secure, Governed, and Compliant by Design

We know enterprise AI cannot be a compliance liability. DataSum embeds security, governance, and auditability into every layer of the AI lifecycle. Our approach ensures AI systems are not only intelligent but also safe, auditable, and defensible across regulatory frameworks.

Key Capabilities:
Role-Based Access & Fine-Grained Permissions: Every AI interaction is controlled by RBAC integrated with identity providers like Okta or Azure AD, ensuring access is scoped and auditable.
Audit Logging and Traceability: All model inputs, outputs, and intermediate decision paths are logged with trace IDs. These logs are stored in immutable object storage (e.g., S3 with object lock) and indexed for forensic review.
Prompt Injection & Adversarial Testing: We run prompt security scanners and adversarial test cases using tools like PromptBench and Rebuff to detect and mitigate injection risks, toxic output, and data leakage.
Policy-Enforced Model Access: LLM usage is wrapped in governance layers that enforce token-level rate limiting, prompt structure validation, and IP-sensitive output restrictions.
Compliance Integration: DataSum’s systems are aligned with leading frameworks—SOC 2 Type II, HIPAA, GDPR, ISO 27001. We implement data tagging, consent-driven data usage, and model explainability hooks to meet regulatory obligations.
Outcome:

Trustable AI that’s not only smart—but secure, transparent, and built to withstand audits, compliance checks, and real-world scrutiny. cannot be a compliance liability. DataSum embeds security, governance, and auditability into every layer of the AI lifecycle.

Our Services

Smart. Scalable. Future-Ready.

Artificial Intelligence

Unlock new possibilities with AI solutions tailored to your unique business needs. Wherever you are on your AI journey, we help you innovate, optimize, and grow with confidence.

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Digital Transformation

Embrace the future with digital strategies designed to simplify workflows, enhance customer experiences, and position your business as an industry leader.

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Platform & Integrations

Empower your organization with platforms and integrations that drive productivity, scalability, and seamless operations across teams and systems.

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Digital Product Engineering

Turn ideas into reality with our full-spectrum product engineering services, from initial concept to a market-ready product that delivers impact.

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Support Services

From proactive infrastructure management to tailored tech solutions, our support services help your business thrive by addressing its core operational needs.

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Technology Solutions

Leverage our extensive expertise to craft adaptable, forward-thinking technology solutions that meet the evolving demands of your industry.

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Industries We Serve

Adapting to Change, Advancing with You

Infrastructure

ISVs

Fintech
Healthcare

Healthcare
Contact us to discuss your challenges or simply talk to our engineers

    DataSum is an AI-first product engineering company, specializing in data-driven software solutions. We craft scalable, intelligent systems that power business growth and innovation.

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    Locations

    • India  - Pune
    • USA – San Francisco

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