Sookshum Labs
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AI Engineering
AI Agents & AutomationsOn-Device & Edge AIAI-Powered SaaSAI Consulting
ScoreSmart.ai
Ecommerce Engineering
MagentoShopifyHyvä
BST Group
Product Engineering
FlutterFull-Stack Web App
RapidStock
Product Design
UI/UX DesignDesign System
ScoreSmart.ai
Platform & Integrations
Headless CMSAdmin and Low-CodeAPIs and MigrationsWebflowWordPress
Atento Shops
Managed Engineering
Upgrades and EOLRetainers and SLAsMonitoring
The Instrument Place
AI Consulting

Advice From The People Who’d Actually Build It.

Most AI consultants hand you a deck and disappear. We are engineers first. The same people who scope your architecture also ship the code, so the plan you get is one you can actually execute.

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What Does an AI Strategy From Us Look Like?

We look at where AI touches your workflows, data, and team, then tell you where it creates real ROI and where it does not. You get a concrete implementation roadmap, not a slide deck you have to translate into a plan.

  • AI opportunity and use-case discovery
  • Custom AI strategy aligned to business goals
  • Data, technology, and infrastructure assessment
  • ROI-driven roadmap for AI adoption
The risk

The AI Strategy Gap in Your Roadmap

A strategy deck isn’t an AI implementation plan. Without the right technical direction, teams lose months navigating tools, vendors, data, and unclear priorities.

Strategy Without a Build Plan

AI initiatives often stall between business goals and technical execution, leaving teams with ideas but no clear path to production.

One-Model Dependency

Building around a single AI provider can create unnecessary vendor lock-in, unpredictable costs, and limited flexibility as models evolve.
What we do

What We Do on an AI Consulting Engagement

Six kinds of work depending on where your team is in the AI journey. Most engagements start with one and pull in others once the scope is clear.

AI Strategy Development

We start with the actual business problem and the data behind it. One conversation, then a short written brief: what you are trying to do, what you have to work with, and where the real blockers are.

AI Opportunity Mapping

We map where AI can move the needle in your specific operations: the high-volume, low-judgment tasks it handles well, the workflows where it creates risk, and the ones it has no business touching.

Domain-Specific Model Development

We select and fine-tune a foundation model against your proprietary data. The model choice is justified against the task, not based on which one is trending. We test it on real inputs before it goes near production.

Generative AI Solutions Development

We design and build the AI feature from the interface to the model call: prompt design, retrieval, grounding, and the fallbacks that decide what happens when confidence is too low to give an answer.

Data Engineering

We handle the data pipeline from source to model-ready: cleaning, structuring, labeling, and validation. If the data is not ready, the model will not be either, and we say so before anyone writes a prompt.

AI Integration

We wire the AI feature into your existing systems: APIs, auth, access controls, and logging. The handoff includes documentation an engineer who was not in the room can follow.
our process

Our Discovery Process

Four stages, each with a defined output.

Assessment Phase

Where you are now: data quality, team skills, current tooling, and the gap between where AI would help and where it would not.

Strategy Development

A written strategy scoped to your actual use case, covering build vs. buy decisions, cost, timeline, security, and privacy requirements.

Data Collection and Preparation

We audit what you have, identify what is missing, and prepare the data for training or retrieval. If it is not ready, we say so here, before build starts.

Custom Model Development

We fine-tune and integrate the model against your data and test it against known-correct outputs before anything ships.

OUR WORK

One Real Engagement, Not Just A Pilot Deck.

ScoreSmart.ai is the one AI engagement we can point to today, strategy through build. The DEMO cards below show how we approach a consulting engagement. None of them has a client name or stat attached.

Case Study

AI golf performance analytics that never invents a number

A golf performance and coaching platform for players and coaches. Its AI writes insights, and every number in them is checked against the database before anyone sees it.

100%of AI-written numbers checked against the database

Industries We Serve

Where We’ve Scoped and Shipped AI.

Five sectors where we have done the strategy work, then stayed to build it.

Healthcare

Healthcare platforms where AI strategy has to account for patient data, compliance, and the humans who override the model. We scope it with all three in mind.
Patient portalsBooking and schedulingHealth content

Ecommerce & Retail

Retail and commerce teams where AI can lift search, support, and personalization, once the data governance is in place to make it defensible.
Multi-Channel Retail PlatformsB2B platformsB2C solutions

Fitness & Sports

Sports and fitness platforms where AI reads activity and engagement data. We scope what is feasible on the real data and build it into the product.
Fitness Tracking AppsPersonal Training & Coaching SolutionsActivity and Performance Tracking Solutions

Business Solutions

Internal tools, CRMs, and back-office systems where the strategy question is always the same: automate which parts, and who signs off on the output.
CRM AppsProject Management AppsCustom Business Solutions

On-Demand Solutions

Learning and scheduling platforms where AI personalizes and routes. We scope the model choice, the data prep, and the fallback before anything gets built.
Online Classes AppE-Learning PortalsSchool Management
Testimonials

What Clients Say

Teams we have scoped and built AI for, on what the engagement looked like and what shipped.

Talk to the practice, not a sales queue.

30 minutes with an engineer who has shipped this exact problem before.

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