View Project

Machine Learning Development Services
Built Around Your Business Goals

Shanti Infosoft provides Machine Learning Development Services that drive measurable business impact. From predictive models and NLP to computer vision and MLOps, we build production-ready solutions backed by 13+ years of experience.

Book a Free ML Readiness Assessment See Our Work

4.8/5

4.9/5

5/5

13+

Years of AI Engineering

700+

Projects Delivered

690+

Clients

35+

Industries

Level 5

CMMI Level 5 Certified

Is Custom ML Development Actually
What You Need?

A straight answer before the sales pitch — when machine learning solves your problem, and when it's the wrong tool for the job.

ML Development Makes Sense When

Rules-based workflows fail on exceptions, and you have enough data to train a smarter system.
You have years of business data but struggle to turn it into actionable decisions.
Forecasting accuracy impacts revenue, inventory, staffing, or operational planning.
You need to classify, extract, or analyze large volumes of documents, images, emails, or conversations.

ML Development Is Premature When

Your data is limited, poorly labeled, or doesn't reflect real-world conditions.
The problem can be solved with simple rules, workflows, or process automation.
Success isn't tied to a measurable business outcome or performance metric.
You lack the operational process needed to deploy, monitor, and improve models.

Custom Machine Learning Development
Services Full Lifecycle

From data readiness and model development to deployment, monitoring, and optimization we build custom ML solutions tailored to your business goals.

87%

Machine Learning Projects Fail Before Delivering Business Value

Building models is easy. Building models with clean, reliable, and usable data is not. Assess your data quality, labeling, and infrastructure before investing in ML development.

Custom ML vs. Off-the-Shelf
AI How to Decide

Choose the Right AI Approach for Your Business Goals, Data Complexity, Scalability, Performance, and Long-Term Competitive Advantage.

Off-the-shelf AI tools — OpenAI APIs, AWS SageMaker prebuilt models, Google AutoML, Salesforce Einstein — are the right choice when

Your use case is standard and your data matches what those models were trained on
You don't need decision-level explainability
You can accept the accuracy ceiling the vendor's model imposes

Custom machine learning development is the right choice when

Your domain has specialized vocabulary, signal types, or failure modes generic models don't handle — clinical text, industrial sensor data, financial instrument behavior
You need accuracy above what commodity models achieve and you have the data to do better
Regulations require documented explainability, per-decision audit trails, or data residency guarantees SaaS vendors can't provide
Model performance is a competitive differentiator, not just a utility cost

The decision isn't always binary. Many of our most effective production systems combine a pretrained foundation model with custom fine-tuning and domain-specific post-processing — giving you speed and cost advantages while hitting the accuracy and compliance requirements of a fully custom build.

ML Solutions by Use Case —
Problem → Data → Model → Result

Generic industry lists tell you nothing about whether ML applies to your situation. Here's what the work actually looks like.

Looking for a Reliable AI Development Company?

Partner with a team that delivers scalable, production-ready AI solutions tailored to your business needs—from strategy to deployment and beyond.

Book a Free Assessment

Problem : Clinical documentation consumes 30% -40% of physician time, creating burnout and care delays.
Data : EHR text, clinical notes, SOAP documentation, ICD-10 coding history.
Model : Fine-tuned clinical NLP with domain-adapted vocabulary and HIPAA-compliant processing architecture.
Result : 35% - 50% reduction in physician documentation time. Human review retained for final sign-off. Full audit trail on every generated record.

Problem : Fraud rules are static. Fraud patterns evolve faster than rule updates.
Data : Transaction logs, behavioral signals, device fingerprinting, network graph data.
Model : Real-time ensemble fraud scoring processing 100,000+ events per minute at sub-50ms latency.
Result : 25% -40% improvement in fraud catch rate versus rule-based systems, with false positive rates held within card services' operational tolerance.

Problem : Inventory planning runs on gut feel and historical sales data, not forward signal.
Data : POS transaction history, seasonal patterns, marketing calendar, supplier lead times, external demand signals.
Model : SKU-level demand forecasting with 8% -12 week horizon and per-forecast confidence intervals.
Result : Clients average 15% -25% reduction in both stockouts and overstock. Markdown losses reduced proportionally.

Problem : Maintenance is calendar-based. Unplanned downtime is expensive.
Data : IoT sensor readings — vibration, temperature, pressure, acoustic — with historical failure labels.
Model : Predictive maintenance classifier with multi-class failure mode detection and lead-time estimation.
Result : 30% -45% downtime reduction in production deployments. Maintenance teams shift from reactive repair to planned intervention windows.

Problem : The product generates rich usage data but customer health scoring is manual and subjective.
Data : Product usage logs, support ticket history, billing history, feature adoption patterns.
Model : Customer health score model with leading indicator weighting tuned to your churn timeline.
Result : Customer success teams shift from reactive firefighting to proactive outreach 45% -90 days before predicted churn signals emerge.

Problem : Route optimization runs on static rules that don't account for real-time conditions.
Data : Historical delivery data, traffic patterns, vehicle capacity, time windows, live event feeds.
Model : ML-enhanced route optimization combining operations research with learned pattern adjustment.
Result : 12% -20% reduction in fuel and mileage cost with improved on-time delivery rates in the first 60 days.

Data Requirements Before Starting
ML Development

Most ML projects that fail do so because of data problems nobody was honest about early enough — not because the model was wrong.

Volume

Do you have enough labeled historical examples?

Fraud detection needs thousands of labeled fraud events. Defect detection computer vision needs thousands of labeled images per defect class. Volume requirements vary significantly by model type — we specify exactly what's needed.

Quality

Is the data consistent, accurately labeled, and representative of what the model will see in production?

Missing values and label noise are addressable. Systematic historical bias — where data reflects decisions made by a flawed process — is a more serious problem that requires explicit debiasing work.

Labeling status

For supervised ML, do you have labeled examples of the outcome you're predicting?

If not, we scope the labeling effort explicitly — cost, timeline, tooling — before committing to model development.

Distribution match

Does your historical data reflect current production conditions?

Training on 2021% -2022 data to predict 2026 behavior in a market that's shifted structurally introduces silent failure risk from launch day.

Lineage and governance

Can you document where training data came from, who had access, and how it was processed?

In regulated industries this isn't optional — it's an audit requirement. If your data isn't ready, we'll tell you and scope the engineering work to make it ready. We don't start model development on data we can't stand behind.

Who This Service Is For

Designed for teams and organizations looking to scale with AI.â€¨ Built for businesses seeking reliable, data-driven solutions.

Early-stage startups (pre-Series B)

You need the one or two ML features that differentiate your product — built efficiently, without over-engineered infrastructure. You don't need a full MLOps platform yet. You need a model that works in production and a clear retraining plan.

Growth-stage companies (Series B-D)

You have data, product-market fit, and real competitive pressure. ML extends your advantage — better recommendations, smarter scoring, faster document processing — without a 12-month enterprise onboarding cycle.

Enterprise and mid-market

Complex data infrastructure, regulatory requirements, stakeholder governance. We've delivered inside these constraints across 35+ industries. Compliance architecture and board-level explainability are standard parts of our delivery, not line items you add later.

Data-rich SMBs

Years of operational data, watching larger competitors pull ahead with ML. You don't need an enterprise platform. You need well-scoped machine learning solutions that move specific KPIs. We have a delivery model built exactly for this.

A focused ML model with data ready ships in 6% -12 weeks. Full custom machine learning development — data engineering, training, integration, and MLOps services — runs 14% -24 weeks. Enterprise machine learning solutions across multiple use cases deliver in phases over 6% -12 months. ML model audit and rescue engagements go from diagnosis to re-deployment in 4% -8 weeks.

How We Measure ML Success
Metrics That Matter

We don't hand over an AUC-ROC score and call it delivered. Here's what success looks like across every ML engagement.

Business outcome
KPI

The headline metric. Revenue protected, cost reduced, hours saved, error rate dropped — in numbers your finance team recognizes.

Accuracy against your operational threshold

Not a benchmark. Your acceptable false positive rate, your precision/recall tradeoff, your defect escape rate limit.

Inference latency

Against your pipeline SLA. Sub-50ms for real-time fraud scoring. Under 30 seconds for document processing. Whatever the use case requires.

Drift timeline

How long before the model needs retraining? We document this at delivery and automate the detection so you're not flying blind.

Cost per inference at production volume

Particularly important for SaaS companies where model cost scales with usage. We optimize this explicitly.

Explainability quality

Can the model's decisions be explained to a regulator, auditor, or board member in plain language? We deliver SHAP reports and confidence scores that make this possible on every prediction.

AI Projects We've Built for USA
& Global Clients

We don’t just claim to be a top AI company—we prove it with real, production-ready results. Here are three AI systems built and deployed for real clients.

Mon.AI AI Clinical Assistant for US Healthcare

Challenge

Doctors spent 35% - 40% of their time on documentation and clinical decision lookup — time that should be spent on patients.

Solution

AI clinical assistant that listens to consultation audio in real time, summarizes EHR context, suggests differential diagnoses, flags drug interactions, and auto-generates clinical notes — all HIPAA-compliant and integrated into the existing EMR system.

Outcome :

40% reduction in documentation time per patient visit
28% improvement in early warning detection for high-risk patients
NPS from physicians increased by 34 points post-deployment

View Project

Boodle All-in-One Personal Finance App

Challenge

People were juggling multiple banking apps and spreadsheets to manage money — leading to poor budget visibility, missed savings goals, and financial stress.

Solution

All-in-one fintech mobile app that consolidates multiple bank accounts, enables goal-based savings, delivers AI-driven financial insights, tracks income and expenses, and supports seamless inter-bank transfers — all within a single intuitive interface.

Outcome :

45% improvement in users meeting monthly savings goals
3.2x increase in daily active engagement
App Store rating of 4.7★ achieved within 60 days of launch

View Project

Truckmate On-Demand Relocation Platform

Challenge

The residential and office relocation process was fragmented and opaque — users had no reliable way to find verified drivers, compare pricing, or track moves in real time, resulting in stressful and costly experiences.

Solution

On-demand moving platform where users submit their relocation details, receive competitive bids from verified drivers, and book the best fit — covering home and office moves with real-time tracking, secure payments, and a fully transparent booking flow.

Outcome :

52% reduction in average time-to-book a verified driver
Driver verification system reduced disputes & cancellations by 38%
User trust score (post-move survey NPS) reached +41

View Project

Clipt Premium Nail Care Booking Platform

Challenge

Booking nail care services was messy and unreliable — clients had no easy way to find trusted professionals, get smart recommendations, or match with the right person, leading to missed appointments and a poor overall experience.

Solution

Outcome:

6-month end-to-end delivery across all platforms
AI matching cut wrong bookings down by 40%
Clean, accessible design earned a 4.7 app store rating

View Project

Rabbit Hole On-Demand Bakery Delivery Platform

Challenge

Ordering baked goods was slow and frustrating — customers had no easy way to place custom orders, get smart product ideas, or know when their delivery would arrive, leading to missed orders and a poor overall experience.

Solution

An AI-powered bakery platform where users get personalized product suggestions, place custom cake orders with smart design help, and pay through a built-in wallet — covering multi-zone delivery with live tracking, smart driver routing, and a simple AI-assisted order experience available in multiple languages.

Outcome:

6-month full-cycle delivery across all platforms
AI routing cut late deliveries down by 35%
English and Arabic support expanded user reach by 48%

View Project

Battersea House Subscription-Based Online Tutoring Platform

Challenge

Finding and booking the right tutor was scattered and stressful — students and parents had no easy way to get smart teacher matches, track lesson progress clearly, or stay connected with educators, leading to poor results and frequently missed sessions.

Solution

An AI-powered tutoring platform where students get personalized learning plans, match with trusted tutors through smart profiles, and join live sessions easily — covering parent dashboards, AI-generated lesson recaps, and role-based portals with a simple and well-structured learning experience for all users.

Outcome:

8-month end-to-end delivery across all portals
AI tutor matching boosted parent engagement by 44%
Clean, accessible design achieved a 92% satisfaction rate

View Project

One Health Virtual Healthcare Consultation Platform

Challenge

Getting medical help was slow and hard to access — patients had no easy way to understand their symptoms, get smart health guidance, or speak to the right doctor from home, leading to delayed care and a poor overall health experience.

Solution

An AI-powered healthcare platform where patients get smart symptom checks, match with the right doctors through intelligent filters, and attend virtual consultations from home — covering health tracking, easy appointment booking, and AI-generated health summaries within a simple and accessible experience built for all users.

Outcome:

Full design and development delivered at launch
AI symptom guidance cut patient drop-off by 36%
Simple navigation achieved a +43 post-visit satisfaction score

View Project

Cure Me Holistic Wellness & Alternative Therapy Platform

Challenge

Finding the right wellness therapist was confusing and time-consuming — users had no easy way to get smart practitioner matches, understand their treatment options, or track their healing progress over time, leading to poor results and unfinished care journeys.

Solution

An AI-powered wellness platform where users get personalized therapy suggestions, match with trusted holistic practitioners through smart profiles, and book sessions in seconds — covering AI-tailored treatment journeys, whole-body progress tracking, and easy appointment management within a calm and simple healing experience for all users.

Outcome:

AI-powered design system delivered across all screens
Smart AI matching improved booking conversions by 39%
Clean, accessible UI achieved a 4.8 user satisfaction score

View Project

Six-Phase ML Development Process

From strategy and data engineering to deployment, monitoring, and continuous optimization — our six-phase machine learning development framework is designed for real business environments, not experimental demos.

Business Outcome Definition

We start with the business problem, not the model. What KPI are we moving? What does success look like in a number a CFO would recognize — not a validation accuracy score? Outcome, acceptable performance thresholds, organizational constraints, and regulatory requirements are all documented before technical work begins. This phase prevents the most expensive ML mistake: building a model that performs well in evaluation and moves nothing in production.

Data Audit & Engineering

Every data source is inventoried, profiled, and assessed for quality and production readiness. Our data engineering team builds the pipelines to clean, normalize, label, and version training data. For healthcare clients this includes HIPAA-compliant anonymization. For financial services clients this includes GLBA-aligned data handling documentation. Data lineage tracking begins here and persists through the full model lifecycle.

Architecture & Model Selection

We present multiple architecture options with honest tradeoffs — accuracy versus latency, interpretability versus performance, cloud-native versus hybrid. You choose based on full context, not just our recommendation. Infrastructure is scoped to your cloud provider and regional data residency requirements before model work begins.

ML Model Development & Evaluation

Engineers build and train models in TensorFlow, PyTorch, HuggingFace, or scikit-learn — or fine-tune LLMs using PEFT/LoRA — depending on the use case. Every experiment is tracked, reproducible, and documented in MLflow. Bias audits run before any model advances to integration. Results are reported in business language alongside technical metrics.

Integration & Production Deployment

Validated models integrate into your production environment via APIs, dashboards, mobile applications, or embedded features in existing enterprise software. Deployment uses zero-downtime strategies, staged rollouts, and security hardening. Every US, UK, Australian, Canadian, and UAE deployment is scoped for regional data residency compliance from infrastructure up.

Monitoring, Drift Detection & Continuous Improvement

Real-time performance monitoring, automated drift detection, and threshold-triggered retraining are implemented as standard — not optional add-ons. Quarterly roadmap reviews identify retraining needs, accuracy decay, and expansion opportunities based on production data accumulated since launch.

If a process is repetitive, data-driven, or decision heavy-AI can optimize it.

We help you do things the right way—aligned with your business goals, delivering real impact, and built to scale

Rewards & Recognition

Globally recognised certifications, platform ratings, and industry awards that validate our commitment to quality, excellence, and client satisfaction.