Enterprise AI Engineering

Intelligence, architected for production.

PplTech is an enterprise AI development company. We design, build and operate custom machine learning, computer vision, generative AI and agentic systems, and stay on to run them once they carry production load.

Machine Learning
Computer Vision
NLP & Document AI
Generative AI / RAG
Agentic Automation
Edge & Embedded AI
MLOps

50+: AI models deployed; Across production environments
99.9%: Model uptime; Measured across the fleet
24/7: Operations support; Dedicated engineering cover

01Capabilities & Sectors

Every sector breaks AI in a different way.

We work across six capability areas: machine learning, computer vision, document intelligence, generative AI, autonomous agents and edge systems. What changes between industries isn't the technique. It's the constraint the system has to survive once it carries load, and the systems it has to integrate with to be worth anything.

The constraint

01 / 06

Inspection has to return a keep-or-reject call inside the cycle time, and defect classes are rare and badly imbalanced. The decision has to reach the PLC, not a dashboard read at end of shift.

Typical first engagement: A single defect class on one line, run in parallel with human inspection until it beats the existing baseline.

Computer Vision

Surface defect detection, assembly verification and dimensional gauging at line speed, with traceability written back to MES over OPC-UA.

Machine Learning

Predictive maintenance on vibration, thermal and current signatures from process historians, plus yield and OEE modelling across lines and shifts.

Edge & Embedded AI

Inference on the cell itself, where a cloud round trip would miss the reject gate and a network outage would otherwise stop the line.

Autonomous Agents

Non-conformance triage and supplier corrective-action workflows, routed to a human reviewer at defined quality gates.

The constraint

02 / 06

Promotional lift, cannibalisation and a long tail of slow movers break naive forecasting. The penalty for a stockout is not symmetric with the cost of markdown.

Typical first engagement: Forecasting for one category, benchmarked against the incumbent planning process before it touches replenishment.

Machine Learning

Forecasting at SKU-location-day grain with promotion and cannibalisation modelled explicitly, feeding replenishment decisions back into the ERP.

Generative AI

Catalogue enrichment and attribute extraction across inconsistent supplier feeds, and assistants grounded in live inventory and pricing.

Computer Vision

On-shelf availability, planogram compliance and shrink detection from the existing store camera estate, without a new hardware programme.

Document Intelligence

Mining reviews, contact-centre transcripts and returns reasons to surface emerging product defects ahead of the warranty curve.

The constraint

03 / 06

Supervisory guidance on model risk makes a model examinable: documented validation, monitored performance and a defensible explanation for any adverse decision. Accuracy alone does not pass review.

Typical first engagement: A challenger model run alongside the incumbent rules engine, measured on alert volume at matched recall.

Machine Learning

Transaction monitoring and fraud models tuned to cut false-positive volume without losing recall, with feature attribution retained per alert.

Document Intelligence

KYC and onboarding extraction, covenant analysis across facility agreements, and monitoring of regulatory change by jurisdiction.

Generative AI

Policy assistants restricted to your own documentation, answering with the clause cited so a reviewer can audit the path to any response.

Autonomous Agents

Reconciliation, dispute handling and reporting workflows, with mandatory approval gates on anything that moves money or files a return.

The constraint

04 / 06

Labels are expensive and clinically contested, and a false negative carries nothing like the cost of a false positive. Everything has to move through the record system rather than around it.

Typical first engagement: A retrospective study on historical records, with clinical sign-off on the evaluation before anything touches a live workflow.

Computer Vision

Imaging triage and worklist prioritisation across DICOM pipelines, assisting the reporting clinician rather than replacing the read.

Document Intelligence

Extraction from clinical notes, discharge summaries and referrals into HL7 and FHIR-conformant structures, with coding support.

Machine Learning

Deterioration and readmission risk stratification, plus no-show and capacity forecasting for scheduling and staffing.

Generative AI

Summarisation across patient history and literature with provenance retained on every assertion, under de-identification controls.

The constraint

05 / 06

A plan computed at dispatch is stale by the second stop, and connectivity is intermittent by design. Telematics, TMS and driver apps routinely disagree with each other.

Typical first engagement: ETA prediction on one lane or region, scored against current promise accuracy before it ever reaches a customer.

Machine Learning

ETA and dwell-time prediction, route and load optimisation, and capacity forecasting that replans against live traffic and yard conditions.

Computer Vision

Number-plate recognition and gate automation, load and damage verification at handover, and in-cab driver behaviour monitoring.

Edge & Embedded AI

In-vehicle inference that keeps deciding through dead zones and reconciles state with the platform when the link returns.

Autonomous Agents

Exception management across dispatch, carrier and customer notification the moment a shipment leaves its plan.

The constraint

06 / 06

Assets are distributed across difficult terrain and inspection is physical and expensive. Failure is measured in outage minutes and regulatory penalty rather than in accuracy points.

Typical first engagement: Inspection triage on an existing image or drone archive, so value is proven before any new capture programme is funded.

Machine Learning

Load and generation forecasting, asset failure prediction from SCADA and condition-monitoring telemetry, and non-technical loss detection.

Computer Vision

Drone and fixed-camera inspection of transmission corridors, substations and solar assets, replacing scheduled manual survey.

Edge & Embedded AI

Inference at substations and remote sites with no reliable backhaul, forwarding only what changes rather than the full stream.

Document Intelligence

Permit, compliance and incident reporting across regulatory regimes, asset classes and jurisdictions.

Working in a sector that isn't listed here? Describe the constraint rather than the industry. That is what determines whether an approach transfers.

02Scope of Work

Three ways to start.

Most engagements begin with a short, fixed-fee discovery so both sides can judge feasibility before committing budget to a build.

012–4 weeks

Discovery & feasibility

Fixed scope, fixed fee

A bounded assessment before anyone commits to a build. We audit your data, test technical feasibility against a real sample rather than a demo set, and model the cost and return honestly, including the case for not building it.

You receive

Data readiness and quality audit
Technical feasibility report with benchmarks
Target solution architecture
Cost, timeline and ROI model

028–20 weeks

Build & deployment

Milestone-based delivery

An embedded engineering team takes the agreed architecture to a running system inside your environment, cloud, on-premise or air-gapped, with the test coverage, documentation and handover needed for your team to own it.

You receive

Production system and full source code
CI/CD and MLOps pipelines
Evaluation suite and performance benchmarks
Documentation and engineering handover

03Ongoing

Managed AI operations

SLA-backed retainer

Models drift, upstream data changes and vendors deprecate APIs. We monitor accuracy against agreed thresholds, retrain on a schedule, and extend systems already carrying production load.

You receive

Accuracy and drift monitoring
Scheduled retraining and revalidation
Incident response against an SLA
Quarterly performance and roadmap review

You own the IP

Source code, trained weights, pipelines and documentation transfer to you. No licensing trap on the models we build for you.

Deployed where you need it

Cloud, on-premise, VPC-isolated or fully air-gapped. Data residency and sovereignty requirements are a design input, not an afterthought.

Evaluated, not demonstrated

Every engagement ships with a benchmark suite so performance is a measured number you can re-run, not a claim in a slide deck.

Not sure which of these you need? That's what discovery is for.

03Approach

The part most AI projects skip.

AI should be within reach of organisations that don't have a research lab: practical, accountable, and owned by the business running it.

Most AI projects stall in the gap between a capable model and a working system. We close that gap: taking a defined business problem through data, modelling, integration and operations until it runs reliably without us standing behind it.

Strategic architecture

We start by mapping where AI actually pays off in your operation, then build a roadmap against measurable outcomes rather than a technology wish list.

Precision engineering

Purpose-built models rather than generic wrappers. Every system is designed around your specific edge cases and the real quality of your data.

Ecosystem integration

Systems that embed into your existing stack and talk to legacy infrastructure from day one, without a parallel migration project.

Adaptive evolution

Deployment is the start, not the finish. We put monitoring and retraining loops in place so models keep pace with drift in your data.

Circuit schematic illuminated on a dark workbench

From schematic to shipped system. We own the whole path.

Our mission

To build AI systems that are practical, scalable and honest about their limits, helping businesses solve real problems and create measurable value, rather than shipping demonstrations.

We work in the open with our clients: transparent about method, candid about what a model can and cannot do, and committed to leaving the capability in your hands.

10M+: Data points processed daily
200%: Average client ROI
99.9%: Model uptime
100%: Client-owned IP
24/7: Operations support
50+: AI models deployed

04Contact

Tell us what you're trying to build.

Describe the problem rather than the solution. If AI isn't the right tool for it, we'll say so.

Email: admin@ppltech.co.in
Phone: +91 90091 10199Mon–Fri, 9am–6pm IST
Location: Remote, serving clients worldwide

Why teams work with us

Problems without off-the-shelf answers
Custom AI logic for the cases existing software does not reach.
Built for production
Designed around throughput, cost and reliability under real load, not benchmark conditions.
Outcomes, not deliverables
Engagements are scoped against business metrics you already track.
Senior engineering
A small team with deep learning, embedded and distributed systems backgrounds.
Support past launch
From first prototype through deployment, monitoring and iteration.