UK-Focused AI Consultancy

Your data is sitting idle.
We change that.

Nexlytiq deploys production-grade ML models for UK construction, manufacturing, banking and insurance — turning operational data into measurable commercial outcomes.

Start With a Free PoC Call → View Our Work
Nexlytiq — Live Model Dashboard
0%
Fraud Detected
0%
Churn Prevented
0%
Cost Accuracy
MODEL CONFIDENCE OVER TIME
Anomaly: Claim #4821 flagged for investigation
Churn risk HIGH — 3 accounts need attention
Bid #CW-2290: Cost overrun probability 72%
↑ 65% fraud reduction
Industries We Serve

Select your sector

We tailor our ML approach to the specific data environment, risk profile and commercial pressures of each industry.

Construction site
The Problem

UK construction projects routinely run 30–50% over budget. Commercial directors lack early-stage cost signals, bid pricing relies on gut feel over data, and supply chain disruptions are absorbed rather than anticipated.

Nexlytiq Solution
XGBoost cost overrun prediction trained on 5 years of historical estimates — delivering bid-stage alerts to commercial directors before any procurement commitment is locked in.
22%
Pricing Accuracy Gain
5 yr
Historical Data Used
XGBoost
ML Methodology
Agro-Chemical Manufacturing
The Problem

A global agro-chemical manufacturer operating across multiple countries had significant untapped revenue potential in its existing customer base — with no systematic way to identify which customers were ready for new product cross-sells or current product upsells.

Nexlytiq Solution
ML propensity models built on transactional and behavioural data — identifying high-likelihood cross-sell opportunities for new products and upsell potential for existing ones, deployed across multiple country markets.
$60M
Annual Revenue Uplift
Multi-country
Deployment Scale
Propensity ML
Methodology
Banking
The Problem

Customer onboarding in banking is slow and inconsistent — documents scattered across systems, HR policies buried in PDFs, and new staff spending weeks finding answers that should take minutes.

Nexlytiq Solution
Enterprise RAG pipeline on LangChain and vector databases — enabling instant intelligent Q&A across onboarding documents, HR policies, compliance guides and process manuals.
60%
Faster Onboarding
RAG
GenAI Powered
LangChain
Technology
Insurance
The Problem

Insurance fraud costs UK companies billions annually. Rule-based detection catches only the obvious — sophisticated fraud patterns buried in historical claims data go undetected until losses mount.

Nexlytiq Solution
Isolation Forest anomaly detection trained on historical claims — surfacing unusual patterns invisible to rules-based systems and routing flagged claims to investigation teams automatically.
65%
Fraud Reduction
Isolation Forest
ML Model
Claims Data
Data Source
OTT Entertainment
The Problem

A major OTT platform was experiencing sustained subscriber churn driven by passive disengagement — users cancelling without clear signals, with retention teams reacting too late and using generic offers that failed to resonate.

Nexlytiq Solution
Customer analytics pipeline combining behavioural segmentation, churn propensity scoring and personalised intervention models — identifying at-risk subscribers early and triggering targeted retention actions before cancellation intent crystallised.
25%
Churn Reduction QoQ
Behavioural
Segmentation Model
Real-time
Propensity Scoring
By the Numbers

Results that speak for themselves

0+
Years delivering enterprise ML across 6 industries
0%
Maximum fraud claim reduction achieved for an insurance client
$0M
Annual revenue uplift from ML-driven cross-sell and upsell models
£0M+
AI portfolios managed annually across enterprise clients
Proof Points

Real deployments. Real numbers.

Insurance
Insurance fraud
Fraud Investigation System

Isolation Forest anomaly detection on historical claims — surfacing complex fraud patterns invisible to rule-based systems and routing flagged claims to investigation teams automatically.

65%
Fraud Claim Reduction
IF Model
Anomaly Detection
Banking
Banking
Customer Onboarding RAG Pipeline

Enterprise RAG system on LangChain and vector databases enabling instant Q&A across onboarding documents, HR policies and compliance guides — eliminating hours of manual search.

60%
Faster Onboarding
GenAI
RAG Powered
Agro-Chemical
AgroChemical
Cross-Sell & Upsell Revenue Engine

ML propensity models for a global agro-chemical giant — identifying cross-sell opportunities for new products and upsell potential for existing ones across multiple countries and customer segments.

$60M
Annual Revenue Impact
Multi-country
Deployment Scale
Construction
Construction
Bid Pricing Intelligence

XGBoost cost overrun forecasting across 5 years of project data — delivering commercial risk alerts at bid stage before procurement commitments are locked in.

22%
Pricing Accuracy Gain
XGBoost
ML Model
How It Works

From messy data to live model

Every engagement follows the same six-stage framework — no shortcuts, no ambiguity. Timelines below reflect a full end-to-end production project (6–12 months). Scoped PoC engagements run significantly faster.

01Discovery & Scoping
We map your data environment, identify data quality gaps, define measurable KPIs and align on commercial outcomes. For a full end-to-end project this phase takes 1.5 to 2 months. For a scoped PoC on limited data, 1 to 2 weeks.
02Data Ingestion & Engineering
We extract, clean and engineer features from your raw data sources into a unified analytical layer — handling the messy reality of real-world industrial and enterprise data.
03Model Training
We develop and iterate ML algorithms — XGBoost, Isolation Forest, LLMs, RAG systems — fine-tuned on your historical data with rigorous cross-validation and bias checks.
04Evaluation & Tuning
We tune for commercial relevance — not just statistical accuracy. The model must answer the right business question and hold up under real operating conditions. We iterate until it does.
05Production Deployment
Containerised, monitored, audited. We ship to production with drift detection, automated alerting and full compliance documentation. Full end-to-end delivery typically takes 6 to 12 months, depending on data quality and project scope.
06Handover & Ongoing Support
Executive dashboards, API documentation, full knowledge transfer and IP ownership. Your team runs it — we remain available for model updates, retraining and new data integrations as your business evolves.
Process
Discovery & Scoping
1.5–2 months (full project) · 1–2 weeks (PoC)
The Team

Built by people who've done the work

We are not generalist consultants who discovered AI last year. We bring deep ML engineering expertise and 20 years of hands-on industrial experience — together.

RK RK CO-FOUNDER
Rohit Khemka
ML & AI Lead · Co-Founder

9+ years delivering enterprise ML across Banking, Insurance, Pharma, Agriculture, OTT and Automotive. Has directed AI portfolios exceeding $150M annually. Deep expertise in LLMs, RAG pipelines, CrewAI agentic frameworks and classical ML — from concept to production.

LLMs & RAGXGBoostCrewAILangChainMLOpsBFSI · Pharma · Agri
MO MO CO-FOUNDER
Mahad Osman
Industrial Domain Expert · Co-Founder

20 years of hands-on operational experience in UK construction — managing assets, supply chains and commercial teams at scale. Translates ML model outputs into boardroom-ready commercial decisions that actually get adopted on site.

UK ConstructionSupply ChainCommercial StrategyAsset ManagementP&L Impact
Get in Touch

Let's talk about your data.

Tell us about a business problem. We will tell you honestly whether ML can solve it — and if it can, what that looks like in practice. No jargon, no overselling.

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Emailhello@nexlytiq.com
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Websitenexlytiq.com
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CoverageUK-Focused Delivery
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