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Production AI that delivers measurable business outcomes

We design, build, and deploy AI/ML + Generative AI systems that automate decisions, reduce cost, and increase throughput integrated into your workflows, secured end-to-end, and monitored in production.

AWS delivery-ready
Full-stack implementation
Cloud/Hybrid/On-prem
MLOps included
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What we do

AI that your teams actually use (because it’s embedded in the workflow)

Predictive & Decision Intelligence

Forecasting, risk scoring, anomaly detection, recommendations, and decision dashboards

Computer Vision & Video Intelligence

Counting, inspection, OCR, safety/compliance alerts—edge or cloud.
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Optimization Engines

Route, workforce, scheduling, slotting, inventory planning—using ML + constraints/optimization.

GenAI for Enterprise Knowledge (RAG + Agents)

Private copilots that work across your documents, databases, and APIs with permissions and governance.

MLOps & Model Reliability

Monitoring, drift detection, evaluation harnesses, retraining pipelines, versioning, safe rollbacks.

Choose Your Fastest Path to Value

Why PIPRA

Product-led AI engineering (not slideware)

PIPRA builds products where AI is core—so we know what it takes to move from demo → adoption → outcomes.

Delivery that survives security review

As an AWS Select Tier Services Partner with experience across AWS and Google Cloud, PIPRA delivers secure, production-ready AI/ML—built to integrate with your systems and pass enterprise security review.

End-to-end implementation under one roof

I success usually fails at integration. PIPRA covers UI + API + DB + deployment, so AI becomes part of the operating system, not a separate tool.

Case Studies

Case Study 1

GenAI Knowledge Assistant (Kuyil-style deployment)

Problem: Teams spend hours searching across documents, databases, and internal tools.
What we built: A private GenAI assistant connecting to enterprise sources (docs/DB/APIs) with on-prem/cloud deployment options.
Business impact: Faster answers, reduced analysis time, and consistent responses across teams.

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Case Study 2

AI-Powered Delivery Acceleration (DevRevive)

Problem: Slow SDLC due to review queues, outdated documentation, and repetitive tasks.
What we built: AI-powered development accelerator positioned to help teams ship up to 40% faster
Business impact: Faster delivery cycles and improved engineering throughput.

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Case Study 3

Track & Trace + Intelligence (RealMeds + Chain4Real)

Problem: Teams spend hours searching across documents, databases, and internal tools.
What we built: A private GenAI assistant connecting to enterprise sources (docs/DB/APIs) with on-prem/cloud deployment options.
Business impact: Faster answers, reduced analysis time, and consistent responses across teams.

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Case Study 4

AI for Cost/BOQ Workflows (QuantifAIBOQ)

Problem: BOQ preparation is time-consuming and error-prone.Slow SDLC due to review queues, outdated documentation, and repetitive tasks.
What we built: AI automation + semantic search workflow; marketed impacts include 60% faster BOQ preparation and 50% fewer errors.AI-powered development accelerator positioned to help teams ship up to 40% faster
Business impact: Quicker submissions and improved bid competitiveness.

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Case Study 5

AI-Camera Based Breach Prevention & Shrinkage Control (Warehouse/FMCG)

Problem:  A multi-brand cosmetics distributor needed to prevent unauthorized entry into high-value rooms and reduce shrinkage during loading/unloading operations.Teams spend hours searching across documents, databases, and internal tools.
What we built: An AI-powered IP camera solution using IP Camera, IoT gateway, AI/ML, AWS, WarePro, and iOS/Android apps—with companion authorization mode, riot/loot detection, photo-based alert escalation, item in/out counting, manifest comparison, and mismatch alerts.A private GenAI assistant connecting to enterprise sources (docs/DB/APIs) with on-prem/cloud deployment options.
Business impact: Built for zero tolerance pilferage, dynamic security, seamless operations, and audit-ready evidence—improving reliability and partner assurance.

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Case Study 6

Autonomous Logistics Orchestration with AI Agents

Problem: Traditional logistics workflows require manual coordination across order processing, packing, and transport booking, leading to inefficient space utilization, slower fulfillment, and higher operational cost.
What we built: A serverless, multi-agent logistics orchestrator on Amazon Bedrock AgentCore that automates order processing, 3D packing optimization, and transport booking, with a React control panel for visualization and status.AI-powered development accelerator positioned to help teams ship up to 40% faster
Business impact: Designed to enable autonomous logistics execution with better vehicle utilization, faster fulfillment, and an architecture positioned for 60–70% cost savings vs traditional approaches through serverless + agent orchestration. 

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How We Deliver

01

Define success metrics

Time saved, error reduction, throughput gains, SLA improvement, cost reduction, risk control.

02

 Build the data foundation

Profiling, cleansing, lineage, access control, governance, and data pipelines

03

 Ship model + workflow integration

APIs, UI embedding, and automation hooks into your existing tools.

04

 Measure, learn, and improve

Offline evaluation + online monitoring, feedback loops, human-in-the-loop where needed.

05

Operate reliably

Drift detection, retraining cadence, performance/cost tuning, and continuous delivery.

Our Offerings