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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written case studies

public client reviews

10 yrs

founder in CRM 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 we have built for

Machine Learning

Challenges Approach Services Process Outcomes Stack

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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

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→

Solar sales, warehouse and commissions moved from Monday.com to Zoho One in under a month

Solar EnergyZoho Customization

< 1 monthfrom Monday.com to live on Zoho One

View case study ↗

“Encloud has been doing an outstanding job on the Zoho project. Their work displays a high level of expertise and attention to detail. They consistently meet deadlines and deliver top-quality results.”

Autargy Solar

Vendor search cut by 75%, and PrimeDumpster's sales rose 40%

Waste ManagementCustom Web Apps

+40%sales, once quotes got fast

View case study ↗

“Time to find the right vendor dropped by 75%, so callers get answers while they are still on the phone.”

Project review

PrimeDumpster, 140 people

Tasmania.com stopped writing quotes by hand and lifted conversion 35%

Travel & TourismMiddleware & Integrations

15+ hof manual work removed every week

View case study ↗

“Encloud were great to work with. They delivered our Zoho CRM automation project on time and budget and to a high quality. Recommended.”

Tasmania.com

Two years embedded in Packt's product squads as their CRM and data engineer

PublishingCRM Engineering

2 yearsembedded in Packt's product squads

View case study ↗

“Encloud has been exceptional for us as a contractor over a full period of 2 years. They embedded themselves in our squads with absolutely no issue. Attentive, professional and they certainly know their stuff. We would not hesitate to re-hire.”

Packt

Zoho solutions shaped around how Ennoble Care actually works

HealthcareZoho Customization

Zohosolutions shaped around the care team's needs

View case study ↗

“Encloud was a pleasure to work with and worked with me to create solutions that addressed our needs in Zoho. They were creative problem-solvers and were able to advise us on the best way to attack each problem.”

K. Lane

Ennoble Care

A custom SuiteCRM module that runs Label LLC's insurance policies the way the team works

InsuranceSuiteCRM Development

1 modulebuilt for insurance policies, fitted to the workflow

View case study ↗

“5 stars all the way, this is the team to use for SuiteCRM. Experienced, did the work in the time I thought was reasonable, and were able to advise us on the correct way to do a few things. They built a custom module to handle insurance policies and set the system up to flow with our workflow.”

S. Meitz

Label LLC

ImageThink's SugarCRM got the custom features and reports its standard setup could not give

Creative ServicesSugarCRM Development

Customfeatures, cross-module fields and repaired reports

View case study ↗

“Encloud's work with our SugarCRM instance was nothing short of spectacular. They helped us build custom features, relate fields across modules and fix reporting issues, and were always willing to jump on a call. I couldn't recommend their work more.”

M. M.

ImageThink

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.

Case studies written with the client named

Public client reviews quoted word for word

10 yrs

The founder building CRM systems

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

12 reviewspublic client reviews on Upwork and direct

“Encloud is great. Their solid knowledge of SuiteCRM was instrumental in several of the projects we were developing.”

Tekriver

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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.