Do we need machine learning, or would simpler automation do?

Simpler automation may be enough. Reporting, business rules or a process change can solve some problems at lower cost. The review compares those options with ML on expected value, data needs and running effort. If ML is not the best fit, the recommendation says so.

We already have data scientists. What would a machine learning consultant add?

A machine learning consultant can offer an external perspective on which use case to fund first or whether a stalled project is worth continuing. The work runs alongside your team, and findings are written for your engineers to use.

Can we start with one business problem?

Yes. A single question, such as whether to fund one use case, can be assessed without a company-wide roadmap. The scope is set around that decision, and wider work can follow later if the findings justify it.

Who from our side needs to be involved?

Usually a business owner for the problem, someone who knows the data, a technical contact for the systems involved and whoever approves the budget. Their input keeps recommendations tied to how your operations and systems actually work.

Do we have to share sensitive data in the first conversation?

No. The first conversation can stay at the level of the problem and the kinds of data you hold. Access, confidentiality and handling requirements are agreed before any detailed data review. Do not share personal or confidential data until access and handling requirements are agreed.

How should we judge an ROI estimate before approving a project?

Check what it assumes: your current baseline, user adoption, data preparation, build and running costs, and how uncertain each figure is. A good estimate shows a range and what would change it. It informs the decision and does not guarantee a return.

Can our own team or another vendor use the recommendations?

That is the intent. Priorities, assumptions and dependencies are written out so whoever takes the next step can follow them. Format and handoff needs should be agreed in scope. If you decide to build, our machine learning development team is one option.

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Machine Learning Consulting Services

Know what to build and whether your data is ready before you commit a budget.

Lucent Innovation helps you choose which machine learning solution is worth funding, confirm your data can support it and plan the investment. The work suits teams starting from an idea and teams with a stalled pilot.

Machine learning development illustration with a robot surrounded by data, analytics and automation icons

Certified Databricks Partner
12+ Years in Business
100+ Specialists on Staff
Rated 4.8 on Clutch

Who We Work with

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When to Seek ML Advice

When Your Business Needs Machine Learning Consulting?

If one of these sounds familiar, a conversation with a Machine Learning Consultant can settle the question before more budget goes in.

01.

Unclear ML Use Cases or Priorities

Teams have ideas for ML but no shared view of which are feasible or valuable and no agreed order for funding them.

02.

Uncertain Data and Infrastructure Readiness

You are not sure whether your data, labels and systems can support a model, or what would need to change first.

03.

ML Spend Without Measurable ROI

Earlier ML work had no agreed success measure, so it is hard to say what it has returned or whether to continue.

04.

Stalled Pilots or Scaling Problems

A pilot works in testing but has not reached production or results have plateaued. An assessment can pinpoint the cause and recommend the next step.

What Our ML Consulting Covers

Lucent Innovation's machine learning consulting services cover the decisions before a build: Which use cases to pursue, whether your data supports them and how to plan the investment. Build work sits with our Machine Learning Development Team.

ML Strategy and Roadmap

Decide where machine learning fits in your business and in what order to invest. The roadmap sets priorities, dependencies and an implementation plan your team or a chosen vendor can follow.

Use Case Discovery and Prioritization

Stakeholder input and data reviews surface candidate use cases. Each is scored on value, feasibility and effort, so the shortlist shows what is worth funding first.

Data and ML Readiness Assessment

Check whether your data has the volume, quality, labels and access a model needs, and whether your infrastructure can support it. The result is a gap list ranked by what blocks progress.

Feasibility and ROI Assessment

Test whether a use case can work with your data and what it would cost to build and run. Findings feed a business case that states its assumptions rather than promising returns.

Architecture, Technology and Vendor Advisory

Compare architecture options, platforms and build, buy or partner routes against your constraints. Criteria and trade-offs are written down so the decision holds up in review.

Governance, Risk and Compliance Advisory

Identify the privacy, bias, security and regulatory questions an ML initiative raises and the controls to plan for. This is advisory input, not legal advice or certification.

Deliverables

What You Get from ML Consulting

A machine learning consulting engagement produces written outputs that leadership, finance and engineering can use to approve, delay or reshape an ML investment. The exact set depends on your scope.

Prioritized ML Opportunity Assessment
Data and Infrastructure Readiness Report
ML Feasibility and Business Case

ML Adoption Roadmap with Cost and Timeline Estimates
Risk Assessment and Success Metrics

ML Consulting Process

How a Machine Learning Consulting Engagement Works

A consulting engagement runs from business discovery to a recommendation your leadership can act on. Scope and length depend on how many use cases, data sources and stakeholders are involved.

Tell us what you want to build or improve, and we'll set up a call to talk about it.

Talk to an ML Consultant

Week 1–2

Align on Goals

Business goals, constraints and decision-makers are agreed at the start.

Week 2–3

Assess Your Current State

Data, systems and past ML work are reviewed for gaps and constraints.

Week 3–5

Evaluate and Prioritize Use Cases

Candidate use cases are scored on value, feasibility and effort.

Week 6–16

Plan the Roadmap

Priorities become a sequenced plan with cost and timeline estimates and stated assumptions.

Ongoing

Recommend Next Steps

Findings and options go to decision-makers, with a handoff to development if you proceed.

Investment Decisions

How Consulting Supports ML Investment Decisions

The findings inform whether to proceed, how much to commit, what could go wrong and how to measure success. Sometimes a machine learning solution is not the right next investment, and knowing that early is a useful result.

Validate Value Before Committing Budget
Surface Technical and Operational Risks
Set a Realistic Budget and Roadmap
Tie ML Work to Business KPIs

Advisory Work behind our ML and Data Projects

Europe

Discovery-Led AI Customer Support for a Shopify Store

Challenge Faced:

Weekly support tickets had grown from 500 to 2,000. Replies passed 24 hours during sales, and routine questions took nearly 70% of agent time.

Solution:

Interviews with support agents and a review of six months of tickets showed which questions repeated most. A look at the store's Shopify workflows then showed where automation could connect without disrupting live operations. The AI and ML assistant that followed uses live Shopify data and a RAG knowledge pipeline on Databricks to answer routine questions and pass complex cases to agents.

Core Technologies Used

DatabricksAWSMySQLNode.jsReactShopifyRAG

Read Full Case Study See All case study

Discovery-Led AI Customer Support for a Shopify Store

Outcomes Achieved

40%

Reduction in response times

30%

Improvement in customer satisfaction

Ways to Work with Our ML Consultants

Choose the arrangement that fits how often you need advice and how many teams the decision touches. Scope and cost depend on your questions, your data and the specialists involved.

Dedicated ML Consultant
$4500/month

Monthly Engagement

Work with a consultant assigned to your organization for recurring ML decisions, such as ranking new use cases, reviewing proposals and updating the investment plan.

✔️Consistent consultant who knows your context
✔️Advice across ongoing ML decisions

Hourly ML Consulting
$40/hr

Hourly billing

Use hourly consulting for a specific question, such as a use case review, a data readiness check, an existing model assessment or a vendor proposal.

✔️Billing based on time worked
✔️Targeted reviews and second opinions

Custom ML consulting team

Team-Based Estimate

Bring ML, data and architecture specialists together for decisions that span several teams, such as a multi-use-case roadmap or a platform choice.

✔️Specialists matched to the questions
✔️Defined roles and responsibilities

Discuss Your ML Consulting Needs

Decide Your Next ML Step with an Expert

Tell us which decision you need help making, our machine learning consultant will take it from there.

Talk to an ML Consultant

What Our Clients Say

A glimpse into what our clients think of the work we've done together.

“No task was impossible, and they delivered. It was so cool to dream big and have the results become a reality, thanks to their dedication, technical expertise, and seamless execution.”

Treva Stone

Moonglow

“Good developers with experienced knowledge and who are always willing to suggest ways to improve your workflow. Their support and expertise have been invaluable in enhancing our project’s efficiency and overall success.”

Gibson Tang

Mighty Jaxx

“We were impressed with their timelines, accuracy, and understanding of business and technical requirements. Their proactive approach and seamless execution made the entire process smooth and efficient.”

Ujjawal Kothari

Iconic

“I am impressed with their ability to get things done quickly while maintaining high quality. They were responsive, easy to work with, and ensured everything was delivered as promised.”

James Owen

Raintree Nursery

“The team truly goes above and beyond to ensure everything looks and functions exactly how we envisioned. And we’re genuinely happy with the results.”

Jack Bensason

Trestique Beauty

“Lucent Innovation dramatically improved our website speed—the integration of crucial features enhanced user engagement and our e-commerce capabilities.”

Akshay Khatri

Go Noise

“After working with multiple vendors, Lucent was the only team that truly understood and transformed our vision into a world-class product. From confusion to clarity they guided, built, and delivered beyond expectations.”

Glenn Freezman

Digital Speaker Agent

“Nicobar's smooth migration was achieved through Lucent Innovation's structured planning and patient approach. They demonstrated their reliable expertise and minimized potential disruptions.”