Machine learning that earns its place in production.

We build models the way we build software: scoped to a KPI, fed by clean pipelines, shipped into the tools your team already lives in, and watched long after go-live.

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40+

models in production

98%

of clients stay with us

years shipping data systems

Churn model

v3 · xgboost

LIVE

TRAINrows: 1.2M · features: 84 · retrain: weekly · drift: none

94.2%

ROC-AUC

+6.1

pts vs v2

38 ms

p95 latency

Teams shipping ML with Encloud

NORTHBRIDGEmeridianStratosVANTAGEorbitaBLUESUMMIT

Machine Learning

Challenges Approach Services Process Outcomes Stack

Book a call ↗

Most ML never makes it
out of the lab.

Four failure modes kill more models than bad math ever does. We engineer against every one of them, on every engagement.

01DATA

The signals live in five systems that don't agree

CRM says one thing, billing another, the warehouse a third. Until the data is connected and continuously validated, any model built on it is guesswork with extra steps.

02VALUE

There's no number the model is supposed to move

A project that starts from a technique instead of a KPI can't prove it worked, and can't defend next quarter's budget. "Interesting" is not an outcome.

03ADOPTION

Great in the demo, invisible in the workflow

Accuracy in a notebook means little if predictions never reach the CRM, the queue or the person deciding. Integration and latency kill more models than math does.

04DRIFT

Accuracy erodes quietly after go-live

Customers change, markets shift, data drifts. An unmonitored model gets confidently wrong, slowly at first, then all at once, usually in front of a customer.

Our fix: ship models like software.

No research theater. Every model we take on gets an owner, a KPI, a pipeline and a monitoring plan, the same discipline we bring to the CRM and middleware systems we've run for nine years.

Pressure-test your use case →

Start from the KPI, not the technique

Before any model, we agree the number it must move and what it's worth. Use cases that can't clear that bar don't get built, we'll tell you so.

Use-case scoringROI thresholdKill criteria

Fix the data before the model

Nine years of CRM and middleware work means we've usually met your data mess before. Pipelines, quality gates and features come first; modelling second.

PipelinesQuality gatesFeature store

Ship into the tools people already use

Predictions land in Zoho, HubSpot, the ticket queue or a dashboard your team already opens, not a separate app nobody logs into.

CRM embeddingAPIsDashboards

Watch it like production software

Every model ships with monitoring, drift alerts, versioning and a retraining plan. If accuracy slips, we know before your customers do.

Drift alertsRegistryRetraining plan

ML services, end to end

All AI & Data services

ML Strategy & Roadmap

Use-case scopingReadiness auditKPI definitionBuild vs. buy

Data & Feature Pipelines

ETL & streamingFeature engineeringQuality gatesWarehouse design

Model Development

ClassificationRegressionRecommendersAnomaly detection

Forecasting & Prediction

Demand forecastsChurn & LTVRisk scoringMaintenance

NLP & Document Intelligence

Ticket routingSummarizationDoc extractionSearch & RAG

Computer Vision

Image classificationObject detectionVisual inspectionOCR

Deployment & Integration

APIs & batchCRM embeddingEdge & cloudDashboards

MLOps & Model Care

Drift monitoringCI/CD for modelsRegistryRetraining

How a model ships at Encloud

Five stages, each with named deliverables. Hover a stage to see what you get.

/ 05

Frame

01Frame the problem

One workshop to name the decision the model improves, the KPI it must move, and what that's worth. We score candidate use cases and pick where to start, or tell you ML isn't the fix.

Scored use-case listReadiness snapshotTarget KPI & baselineGo / no-go call

02Fix the data

We audit your sources, wire the pipelines and put quality gates in front of everything the model will learn from. Boring, decisive work, this is where most projects are won.

Source auditPipeline buildFeature definitionsQuality dashboard

03Build & prove

Train, test and validate against the KPI from step one, on holdout data and then in a shadow run against live traffic. You see the evidence before anything touches production.

Candidate modelsValidation reportShadow-run resultsShip decision

04Wire it in

The model ships into the tools your team already uses, CRM fields, ticket queues, APIs, dashboards, with latency budgets and fallbacks agreed up front.

Production deploymentCRM / app integrationRunbook & fallbacksTeam walkthrough

05Keep it honest

Monitoring, drift alerts, versioning and scheduled retraining keep accuracy where it started. Monthly reviews tie model performance back to the KPI, and queue the next use case.

Monitoring & alertsRetraining scheduleMonthly KPI reviewNext-use-case queue

ML outcomes in spotlight

All case studies

←

→

31% fewer stockouts with demand forecasting across 12 plants

ManufacturingForecasting

meridian

31%fewer stockouts

View case study ↗

“Planning meetings start from the forecast now, not a spreadsheet argument. Reorder points finally reflect reality.”

Daniel Okafor

COO, Meridian Manufacturing

Every support ticket routed, summarized and prioritized by NLP

B2B SaaSNLP

Stratos

2.1×faster ticket resolution

View case study ↗

“Triage that used to eat a team's whole morning happens before anyone logs in, and response quality went up.”

Alicia Grant

Head of Support, Stratos

Lead scoring inside Zoho drives 2.3× faster follow-up

Real EstateLead scoring

NORTHBRIDGE

2.3×faster lead follow-up

View case study ↗

“Sales admin dropped 40% and follow-up is 2.3× faster. The team finally trusts the pipeline.”

Priya Menon

VP Sales, Northbridge Realty

44% less manual document work with vision-based extraction

RetailComputer vision

orbita

44%less manual document work

View case study ↗

“Invoices, delivery notes, claims, read, classified and filed before ops ever touches them.”

Rosa Delgado

Head of Operations, Orbita

27% lower fraud losses from real-time anomaly detection

Financial ServicesAnomaly detection

BLUESUMMIT

27%lower fraud losses

View case study ↗

“Flags land in seconds, not overnight batches. The model is now part of how we underwrite risk.”

James Whitaker

Director of Risk, BlueSummit

Put a senior ML pod on your problem, not a slide deck.

Data engineer, ML engineer and delivery lead, working inside your stack from week one. The same pod stays through go-live and beyond.

40+

Models running in production across client stacks

6 wks

Typical time from kickoff to first validated model

98%

Of clients stay with us year over year

The stack behind the models

Proven open-source first, managed services where they pay for themselves.

Modeling & training

Data & pipelines

Tracking & registry

Serving & deployment

Monitoring & data stores

The modelling toolkit behind classical ML and deep learning alike.

PyTorch

TensorFlow

scikit-learn

Keras

XGXGBoost

Hugging Face

Book a working session, not a sales call.

45 minutes with an ML engineer. Bring a problem and a sample of your data, leave with a feasibility read, a rough architecture and the KPI we'd aim at.

✓No obligation, no prepared pitch

✓NDA on request before you share data

✓Honest "don't build this" when ML isn't the answer

4.9 / 5average across 120+ engagements

“They told us our first idea wasn't worth building, then found the one that was. The forecast model paid for itself in a quarter.”

Daniel Okafor

COO, Meridian Manufacturing

Tell us where it hurts

I'm okay with Encloud contacting me about this request. No newsletters, no list-selling. *

Book my session ↗

We reply within one business day. Your details never leave Encloud.

Frequently asked questions

Still weighing whether ML is right for your business? Bring the question to a working session, with your data in the room.

Talk to an ML engineer →

Can you build ML inside our Zoho, HubSpot or SugarCRM?+

Yes, that's our home turf. Lead and deal scoring, churn flags, forecast fields and call insights land as native CRM fields and workflows, so your team gets predictions without learning a new tool.

How do we know if our data is good enough?+

You usually don't need perfect data, you need connected, honest data. The working session includes a readiness read: we look at a sample and tell you what's usable now, what needs pipeline work first, and what's missing entirely.

What if the model doesn't beat the KPI we set?+

Then it doesn't ship. Step one defines a baseline and a kill threshold; the shadow run in step three proves lift against live traffic before production. You'll never be asked to adopt a model on faith.

How long until we see a working model?+

A validated first model typically lands in about six weeks; production wiring and monitoring add a few more depending on integrations. Data readiness is the biggest variable, we'll give you a real estimate after the session.

What kinds of models do you build?+

Forecasting, churn and LTV, lead and risk scoring, recommendations, anomaly and fraud detection, NLP for tickets and documents, and computer vision for inspection and extraction. If a simpler rule beats a model, we'll say so.

Who owns the models and the code?+

You do, models, pipelines, feature code and documentation all live in your repositories and your cloud accounts. No black boxes, no vendor lock-in, no hostage IP.

What happens after go-live?+

Every model ships with monitoring, drift alerts, versioning and a retraining schedule. Most clients keep the same pod on a light retainer for monthly KPI reviews and continuous improvement, but that's optional, not required.

How do you handle security and compliance?+

Work happens in your cloud with least-privilege access, encryption and audit trails. We're used to HIPAA, GDPR and SOC 2-aligned environments, and we'll sign an NDA before you share anything sensitive.