Data · Analytics · Applied AI
Someone in your organization
is making decisions on data they can't fully trust.
They probably know it.
We find the root cause in your data, your pipelines, or your models, and fix it with a defined scope, a fixed price, and full documentation your team owns when we leave. No retainers. No lock-in. No dependency.
Free assessment · no email required · results in five minutes
LGT Solutions is a specialist technology practice for analytics infrastructure, applied ML, and systems design. We work with a small number of clients at a time, deliberately: the person who scopes the work also explains the trade-offs, builds the system, and answers when reality disagrees with the plan.
Where this usually starts
Common patterns across companies of all sizes that run on data, with or without a dedicated team to own it.
A decision was made on data that turned out to be wrong. You found out too late.
Not a dramatic failure. A quiet one: a report that was off, a forecast built on a broken pipeline, a metric that had been wrong for months. The cost is real and hard to quantify, which makes it easy to defer.
Nobody owns the data, so everyone partially owns it
There's no dedicated data person. Someone in ops or finance handles the numbers on top of their actual job. It works until it doesn't. When it breaks, the path to fixing it runs through four people who all have other priorities.
The last vendor fixed it by making themselves permanent
They built something that works. Nobody on your team understands how. You call them when it breaks, you pay when you call, and you can't change vendors without starting over. That's not a solution. That's a new dependency with a monthly invoice.
How we work. And don't.
These are non-negotiable. Not positioning.
We decline work we can't do well
If a project is outside our depth, we say so before the engagement starts, not after three weeks of discovery.
We build for independence, not dependency
We have never used a proprietary platform that locks you in. Every engagement ends with your team able to run and modify what was built. This is a structural constraint, not a preference.
We work within defined scope, and extend it honestly
If the scope needs to change, we surface it immediately with an explanation and a cost. We don't absorb scope quietly and invoice later.
Responsible AI is a technical standard, not a marketing claim
We don't deploy models that can't explain their outputs, and we don't present AI as certain when it isn't. Confidence ranges, data-quality disclosures, and known limitations are part of the deliverable, not footnotes.
What we build
Five areas. One goal: decisions you can trust.
Data infrastructure
Pipelines, ETL, data modeling, warehousing. The plumbing that makes everything else work. We build it so your team can maintain it, not so you need us to.
Analytics and reporting
Dashboards and reports that run themselves, sourced from a single version of the truth. The goal: nobody has to ask "which number is right?" anymore.
Forecasting and predictive models
Regression models, time-series forecasting, recommendation engines. Models built on your data, calibrated to your domain, documented so you understand what they're doing and why.
Applied AI: grounded and accountable
Language models connected to your internal data (RAG), document analysis, intelligent workflows. We build AI systems that tell you what they don't know. A confident wrong answer is worse than an honest uncertain one.
Platform foundations for data and AI
An opinionated reference architecture for your stack: warehouse, transformation, observability, and AI evaluation.
Built by us · live in production
performance.lgtnow.com ↗Training intelligence for endurance athletes
Personalized training plans and race finish-time forecasts, generated from each user's actual training history. The engine is based on the Banister impulse-response model (a peer-reviewed sports science framework), adapted for real-world use with Apple Watch integration, personal calibration, and confidence ranges derived from session quality and volume.
The forecast shows a range, not a single number. That's deliberate: a confident wrong answer is worse than an honest uncertain one. The model explicitly surfaces what it doesn't know and why.
Model basis
Fitness–fatigue model
Input
Apple Watch + manual
Output
Range, not a point
What you get. Specifically.
Not outcomes language. The actual deliverables, per phase.
- →Written diagnostic report: what is broken, where, and why
- →Root cause analysis, not just symptom description
- →Remediation options with trade-offs, clearly stated
- →Cost and time estimates for each option
- →You can take this document to your board or use it to brief any vendor
- →Fixed scope and fixed price agreed before work starts
- →No scope creep. No surprise invoices.
- →Milestone check-ins so you're never in the dark
- →Written documentation your team can follow without us
- →Runbook: what to do when something breaks, step by step
- →Everything is owned by you, not licensed from us
- →Your team can run, maintain, and modify what we built
- →We're available for questions, but you're not dependent on us
- →No retainer required. No lock-in.
How this differs from a typical engagement
These are structural commitments. Not values statements.
Fixed price before work starts
Most consultancies bill hourly. Your budget is a ceiling on how much they'll spend, not a hard cap.
We tell you when the work shouldn't be done
If the audit reveals a fix that isn't worth the cost, we'll say so. We don't create work to fill an engagement.
You own everything. No exceptions.
No proprietary platforms, no licensed tools, no "you need us to operate this." Your team runs it.
Written deliverables at every stage
Not slides. Not a verbal debrief. A document you can reference, share, and act on independently.
Who we work with
We take fit seriously on both sides. Size doesn't disqualify. Approach does.
Good fit
- +You have a defined problem and want a defined fix
- +Someone on your team will own the outcome after handoff
- +You need to explain this internally: to a board, a manager, or a finance team
- +You value a written record over verbal assurances
- +You want honest advice, including 'this isn't worth doing'
Not a fit
- –You need a managed service or ongoing operational support
- –The scope is open-ended with no defined outcome in mind
- –You're evaluating primarily on price. We're not the cheapest option.
- –You need enterprise-scale delivery teams or 24/7 SLA coverage
Commercial terms
Designed to be straightforward to approve.
Typical investment
Audit
$8,500 – $12,000
~2 weeks, fixed scope
Build
$15,000 – $50,000
Scope and price agreed in writing before work starts
Counsel
$3,500 – $5,500 / mo
Monthly 90-min session, 2-business-day async turnaround, quarterly written review
Scope and price are agreed in writing before work starts. No surprises.
- →Fixed-price contracts: scope and cost defined before work starts
- →Standard MSA and NDA available on request
- →Net 15 or net 30 invoicing
- →All IP and deliverables transfer to you at completion
- →Delivery engagements end at handoff. No ongoing obligation.
- →Retained advisory available separately if needed (Counsel, $3,500–$5,500/mo)
Free · five minutes · no email required
Data and AI Readiness Assessment
Six questions. A scored gap analysis across the dimensions that actually determine whether an AI or data initiative will land: infrastructure, data quality, governance, talent, culture, and AI maturity. You get a breakdown by dimension and your top three priorities.
Take the assessment →Most people take the assessment first. It gives you something concrete to bring to this conversation.Take the free assessment →
Ready to scope it?
Send us a brief description of what you're dealing with. We'll give you an honest answer: whether it's a fit, what an engagement would look like, and what it would cost to find out.
Response within 2 working days. First call is 30 minutes. No preparation required.
hi@lgtnow.comNot sure if it's a fit? Email anyway. We'll be honest.