From “we should do AI” to a
roadmap your board can fund.

A short, structured engagement that inventories every AI opportunity in your business, scores each one by ROI against feasibility, and hands you a costed, sequenced 12-month roadmap. Fixed scope and vendor-neutral, including a written list of what not to build.

Scope your discovery ↗See how pilots get built

written case studies

public client reviews

10 yrs

founder in CRM systems

CRM health audit

20-point review

COMPLETE

Data quality

3,400 duplicate records found

CRITICAL

Automations

12 broken flows firing silently

WARN

✓

Security

Roles follow least-privilege

PASS

VERDICTcritical: 1 · warnings: 1 · passing: 18

checkpoints scored

quick wins found

30 d

fix roadmap

Teams we have built for

AI Discovery, Strategy & Roadmap

Challenges Why Encloud Services Process Outcomes Stack

Book a call ↗

Everyone has AI pressure.
Almost nobody has a plan.

Four patterns show up in nearly every leadership team we sit down with. A structured discovery is the cheapest way out of all of them.

01MANDATE

The board wants an AI strategy by next quarter

Someone senior asked what the AI plan is, and the honest answer is a slide of ideas nobody has costed. Under deadline pressure, teams fund whichever demo looked best last week, and call that a strategy.

02SCATTER

Pilots everywhere, production nowhere

Marketing tried a chatbot, ops has a ChatGPT subscription, an intern built a prototype. Each experiment taught someone something, but none were scored against each other, so budget scatters and nothing compounds.

03DATA

The roadmap was written before anyone checked the data

Most AI plans assume the data exists, is clean and can legally be used. Then the build starts and the model meets reality: half-empty CRM fields, six versions of every customer, and consent nobody collected.

04VENDORS

Every vendor's answer is their own product

Ask a platform vendor what to do and the roadmap ends at their pricing page. Without an independent build-vs-buy view, you either overpay for shelfware or hand-build something $500 a month would have solved.

Our fix: score every idea before anyone builds.

We build AI systems for a living, call intelligence, agents, predictive models, so the roadmap is written by people who know what shipping actually costs. That is also why we can tell you, in writing, which ideas are not worth it.

Pressure-test your AI plan →

Start from the P&L, not the demo

Use cases come from where your business bleeds time and margin, quote turnaround, claim triage, churn, manual re-keying, not from whatever a model demo made look easy. Every candidate is sized in dollars before it earns a place on the list.

Use-case inventoryValue sizingProcess walkthroughs

ROI × feasibility, on one scorecard

Each use case gets scored on expected return and on how hard it really is, data availability, integration surface, model maturity, change cost. The result is a ranked matrix and an explicit kill list, not a wishlist.

Scoring matrixKill listEffort estimates

Check the data before promising the model

A data-readiness assessment runs alongside the workshops: source inventory, field-level quality sampling, lineage, access and consent. If a use case needs data you do not have, the roadmap says so, and prices the fix.

Data-readiness scorecardQuality samplingGap remediation plan

Vendor-honest build-vs-buy

We resell no licenses and take no platform commissions. For every shortlisted use case you get a build-vs-buy memo with three-year TCO on each path, and "buy the $6K tool" is a recommendation we make often.

Build-vs-buy memos3-year TCO modelsNo resale agenda

What a discovery covers, end to end

All AI & data services

Use-Case Inventory & Prioritization

Cross-team workshopsScoring matrixRanked shortlistKill list

Data-Readiness Assessment

Source inventoryQuality samplingAccess & consent reviewGap plan

Build-vs-Buy Analysis

Vendor shortlistsTCO comparisonLock-in review

Risk, Compliance & Governance

Risk registerAI usage policy draftHuman-in-the-loop rules

Quick-Win Pilot Definition

Pilot briefsSuccess metricsGo/no-go criteria

Executive Alignment Workshop

Exec briefing deckDecision frameworkBoard-ready summary

Platform & Architecture Direction

Reference architectureModel shortlistIntegration map

12-Month Roadmap & Budget

Sequenced quartersBudget bandsHiring planDependency map

How a discovery runs at Encloud

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

/ 05

Immerse

01Learn how the business actually runs

Stakeholder interviews across sales, ops, service and finance, plus walkthroughs of the systems where work really happens. Out of it comes a long-list of AI candidates tied to specific processes and their cost.

Opportunity long-listSystems & data mapInterview findings

02Test the data against the ideas

For every serious candidate we sample the underlying data, completeness, accuracy, lineage, consent, and check the integration surface. Ideas that need data you do not have get a remediation price, not silence.

Data-readiness scorecardFeasibility notesRemediation estimates

03Rank ruthlessly, kill openly

A working session where the long-list meets the scorecard: ROI against feasibility, argued in the open with your leaders in the room. You leave with a ranked shortlist and a kill list everyone saw the reasoning behind.

Scored use-case matrixKill list with rationaleShortlist of 3-5

04Settle build-vs-buy and risk

Each shortlisted use case gets a build-vs-buy memo with three-year TCO, a risk and compliance review, and a recommended path. The exec alignment workshop closes this stage with a funded direction.

Build-vs-buy memosRisk register & policy draftExec alignment session

05Sequence the year, brief the pilots

The final artifact: a 12-month roadmap sequenced into quarters with budget bands, hiring implications, a reference architecture, and two or three quick-win pilots scoped to start the week after sign-off.

12-month costed roadmapPilot briefsReference architecture

Discovery outcomes in spotlight

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 engineers on the strategy, not a slide factory.

Your discovery is run by a solutions architect, a data engineer and a delivery lead who build AI systems the rest of the year. The roadmap is priced by the people who would have to ship it.

Case studies written with the client named

Public client reviews quoted word for word

10 yrs

The founder building CRM systems

What we assess with, and what we assess

The discovery evaluates your stack as it is, and shortlists the models and platforms your roadmap should actually bet on.

Model candidates

Data foundations

Systems we inventory

Pilot delivery

Evaluation & measurement

The families we benchmark per use case, hosted, open-weight or fine-tuned, chosen by fit and unit cost.

Claude

OpenAI

LLlama

Hugging Face

Book a strategy session, not a sales pitch.

45 minutes with a solutions architect. Bring your idea list, your board deadline, or nothing at all, leave with our scoring template, an honest read on your data readiness, and a fixed quote for the full discovery.

✓No obligation, no prepared pitch

✓NDA on request before you share anything

✓Vendor-neutral, we resell no licenses

✓Fixed scope and fixed price, agreed up front

12 reviewspublic client reviews on Upwork and direct

“Excellent work in a timely manner. Would use their services again.”

NJ Solar

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Frequently asked questions

Deciding between a discovery, a bigger consultancy, or just building something? Bring the question to a strategy session and get an answer backed by your own data.

Talk to a solutions architect →

How long does an AI discovery engagement take?+

Four to six weeks from kickoff to signed roadmap, with five weeks typical. The window is fixed at scoping: we would rather rank your top use cases decisively than inventory every idea forever. Larger organizations with many departments sometimes run a second, deeper pass on one business unit afterward.

What exactly do we get at the end?+

A scored use-case matrix with an explicit kill list, a data-readiness scorecard, build-vs-buy memos with three-year TCO for each shortlisted use case, a risk register with a draft AI usage policy, two or three quick-win pilot briefs, and a 12-month roadmap sequenced into quarters with budget bands. Everything is yours to execute with us, with another vendor, or in-house.

What does "data readiness" actually mean?+

Whether the data a specific use case needs exists, is complete and accurate enough at the field level, can be accessed without heroics, and can legally be used for that purpose. We sample real records rather than trusting system diagrams, a CRM that is 40% empty on the fields a model needs is the most common finding in our AI readiness assessments.

Do we need a data warehouse before we can do anything with AI?+

Usually not. Plenty of high-ROI use cases, document processing, call summarization, drafting assistance, run against operational systems you already have. A warehouse like Snowflake or BigQuery earns its place when use cases need history joined across systems, and if yours do, the roadmap sequences that work with a price rather than assuming it away.

How do you estimate ROI before anything is built?+

From your numbers, not industry benchmarks: hours spent on the process today, loaded labor cost, error and rework rates, revenue at stake. We model conservative, expected and optimistic cases per use case and state every assumption, so finance can challenge the working instead of taking a vendor slide on faith.

What happens after the roadmap is delivered?+

Whatever you choose. The pilot briefs are written so any competent team can execute them. Many clients have our engineering pods build the first quick wins, that is our AI PoC & MVP and custom LLM integration work, but the discovery is priced to stand alone, and the roadmap never assumes you hire us for the build.

How is this different from a big-consultancy AI strategy?+

It is written by engineers who will quote you a price to build each item, which changes what gets recommended: no 200-page decks, no transformation programs designed to sell the next engagement. Every roadmap line has an effort estimate someone here is prepared to stand behind, and a kill list is a standard deliverable, not a diplomatic omission.

What does an AI discovery cost?+

A fixed price agreed before kickoff, scaled to company size and the number of departments in scope, no day rates, no change orders. For most mid-market companies it costs meaningfully less than one quarter of one misdirected AI hire, which is precisely the mistake it exists to prevent.