Prove your AI concept on your data,
before you bet the budget.

We build scoped AI proof-of-concepts, LLM apps, RAG assistants, predictive models, vision and speech, evaluated against success metrics you sign off before a line of code is written. What passes gets hardened into an MVP real users touch; what fails gets an honest kill recommendation, in writing.

Scope a PoC ↗Explore PoC & MVP services

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

AI PoC & MVP Development

Challenges Why Encloud Services Process Outcomes Stack

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Most AI pilots die between
the demo and production.

Four failure modes kill the majority of AI initiatives before real users ever touch them. Our PoC method exists to close each one in the first fortnight, not month six.

01DEMO

The vendor demo that never survives your data

It dazzled on cherry-picked samples, then fell apart on your abbreviations, half-filled fields and twelve years of PDF scans. A PoC that never ran against messy production records proved nothing, except that a demo can be staged.

02VIBES

Nobody agreed what "works" means

Without accuracy thresholds, cost ceilings and a baseline measured up front, the readout meeting is theater: one stakeholder loved the demo, another saw it hallucinate once, and the project dies in a tie. Success has to be a number, agreed before the build.

03THROWAWAY

A notebook prototype with no path to production

The PoC 'succeeds', then the estimate to productionize it lands at 5x the pilot budget, because it was a notebook with hardcoded keys and no auth, logging or API. Success that has to be rebuilt from zero is just a slower failure.

04STALL

Pilot purgatory

The model works but the pilot never reaches real users, because security review, data access and system integration were postponed to "phase two." The blockers that kill AI projects are organizational, and they must be surfaced in week one.

Our fix: run a PoC as an experiment, not a show.

A PoC is a bet with defined stakes: a metric to beat, real data to beat it on, and a decision at the end, ship, pivot or kill. We engineer the experiment so the verdict is trustworthy either way, and the code survives a "yes."

Pressure-test your AI idea →

Success metrics before code

Week zero produces a metric spec: the number the PoC must beat, the baseline it is measured against, the cost-per-request ceiling, and the kill criteria that end the experiment early. Everyone signs it before the build starts.

Metric specMeasured baselineKill criteria

Your real data, not curated samples

We evaluate on an eval set drawn from your actual records, the misspelled names, the scanned faxes, the sarcastic support tickets. Data access runs under NDA with privacy controls agreed with your security team in week one, not month six.

Real-data eval setEdge-case coverageNDA & privacy plan

Evaluation harness before demo theater

Before you see a demo, an automated harness scores every build against the golden dataset, accuracy, latency, cost per request, failure modes. You watch the score move week over week instead of judging one lucky screen recording.

Eval harnessGolden datasetWeekly scorecards

Production-shaped from day one

The PoC is built as a small service, API-first, containerized, config out of code, on the same architecture the MVP will harden. If the verdict is 'ship,' nothing gets thrown away; the MVP adds auth, monitoring and real users, not a rewrite.

API-first buildContainerized deployZero-rewrite path

From AI idea to shipped MVP

All AI & data services

Feasibility Sprint & PoC Scoping

Use-case triageMetric specFixed-price scope

LLM Application PoCs

Prompt pipelinesGuardrailsCost-per-request model

RAG Prototypes & Knowledge Assistants

Chunking strategyRetrieval evalsGrounded citations

Predictive Model PoCs

Churn & lead scoringDemand forecastingBaseline comparison

Vision & Speech PoCs

Whisper & AssemblyAIDocument extractionAccuracy benchmarks

Evaluation Harness & Benchmarking

Golden datasetAutomated evalsRegression runs

MVP Hardening & Launch

Auth & rolesMonitoring & alertsStaged user rollout

PoC-to-Production Handoff

Scaling planDocs & runbooksFull IP transfer

How a PoC earns its verdict

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

/ 05

Frame

01Frame the bet

One workshop turns the idea into an experiment: the use case, the metric it must beat, the measured baseline, the cost ceiling and the kill criteria, all fixed-price and signed before the build.

Metric specKill criteriaFixed-price scope

02Get the real data in

Access, privacy and security review handled in week one, under NDA. We build the golden eval set from your messiest production records, because that is what the system will face after launch.

Data-access & privacy planGolden eval setBaseline report

03Build against the harness

The PoC ships as a small, production-shaped service, scored by the eval harness on every iteration. You get the scorecard and a working build weekly, evidence, not slideware.

Working PoCWeekly eval scorecardsCost-per-request model

04Call it: ship, pivot or kill

A written readout against the metric spec: what the numbers say, what production would cost to run, and our recommendation, including "kill it" when the data says so. No sunk-cost salesmanship.

Results readoutProduction cost projectionGo/no-go recommendation

05Harden into an MVP

The passing PoC gains auth, monitoring, rate limits and an interface real users touch, rolled out to a pilot group with the eval harness still watching quality in production.

Production MVPLive eval monitoringRollout plan

PoCs that earned their verdict

All case studies

←

→

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 AI pod on the experiment, not a pitch deck.

ML engineer, product engineer and delivery lead from the first workshop through MVP launch. The pod that ran your PoC is the pod that hardens it, no handoff, no re-learning your data.

Case studies written with the client named

Public client reviews quoted word for word

10 yrs

The founder building CRM systems

The stack a verdict is built on

Foundation models where they win, classical ML where it wins, measured, containerized and ready to harden.

LLMs & foundation models

RAG & orchestration

ML, vision & speech

MVP delivery & evals

Frontier and open-weight models selected per use case, with the eval numbers to justify the choice.

Claude

OpenAI

LLlama

Hugging Face

Scope a PoC, not a slide deck.

:45 minutes with an AI engineer, not a sales rep. Bring the use case and a sample of the data; leave with a feasibility read, a draft success metric and a fixed price for the experiment.

✓NDA signed before you share a single record

✓Honest feasibility read, including "don't build this"

✓Fixed price and timeline for the PoC, in writing

✓You own all code, prompts, evals and models

12 reviewspublic client reviews on Upwork and direct

“Encloud delivered outstanding work for us in this change. They are readily available and always willing to help and assist. I would not hesitate to rehire them when we require someone.”

BAC

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Frequently asked questions

Weighing an AI pilot project against waiting another quarter? Bring the question to a scoping call and get an answer backed by your own data.

Talk to an AI engineer →

What's the difference between an AI PoC and an MVP?+

A proof of concept answers one question, does this work on our data, to an agreed metric?, in about four to six weeks. An MVP is the passing PoC hardened for real users: auth, monitoring, an interface, a rollout plan. Because we build PoCs production-shaped from day one, the step from one to the other is weeks of hardening, not a rewrite.

How long does an AI proof of concept take, and what does it cost?+

Most PoCs run four to six weeks from kickoff to a measured verdict; MVP hardening typically adds four to eight more depending on integrations. The PoC is fixed-price, scoped and quoted after a one-week feasibility sprint, so the experiment has a known cost before you commit. No time-and-materials drift.

How do you measure whether the PoC succeeded?+

Against a metric spec signed before the build: the accuracy or quality threshold to beat, the baseline it is compared to, latency and cost-per-request ceilings, and kill criteria. An automated evaluation harness scores every build on a golden dataset drawn from your real records, so the verdict is a number, not a demo impression.

Whose data does the PoC run on, and how is it protected?+

Yours, that is the point. Cherry-picked samples prove nothing. We work under NDA with a data-access and privacy plan agreed with your security team in week one: minimum-necessary datasets, PII redaction where required, your cloud tenancy where preferred, and no client data used to train shared models. Ever.

How does the PoC become a production system?+

Without a rewrite. The PoC ships as a containerized, API-first service on the same architecture the MVP hardens, we add authentication, roles, monitoring, rate limits and a real interface, then roll it out to a pilot group with the eval harness still scoring live traffic. From there, our MLOps and infrastructure services take it to full scale.

What happens if the PoC fails?+

You get a written readout explaining what the numbers showed, why the approach fell short, and what, if anything, is worth trying instead. Roughly one in four of our PoCs ends in a kill or pivot recommendation, and we consider those successes: a five-week experiment that prevents a five-hundred-thousand-dollar mistake did its job.

Who owns the IP, the code, prompts, models and data?+

You do, in full. Code, prompts, evaluation datasets, fine-tuned weights and documentation transfer to you at every milestone, not just at the end. Your data never trains anything we reuse elsewhere. If you take the system in-house after the MVP, we hand over runbooks and support the transition.