Machine learning solutions designed for production
Our machine learning solutions combine model development with data pipelines, evaluation and monitoring. We define the operational decision and ownership needed to take a model into production.
Machine learning solutions are systems that learn patterns from historical data to make predictions or classifications on new data. Unlike language models, they work on structured data such as transactions, sensor readings and customer records, and are typically used for forecasting, risk scoring, classification, anomaly detection and recommendation.
The model works and the project still fails
The pattern is consistent. A data scientist builds a model that performs well on historical data. Then it needs a live data pipeline, a serving endpoint, monitoring, retraining, and integration into the process where a decision actually happens.
That engineering is most of the work and rarely the data scientist’s specialism, so the model stays in a notebook and the project quietly ends.
Build for deployment from the start
We scope machine learning projects around the decision they will inform and the system that decision lives in, before building any model. That constrains the design usefully.
Data pipelines, serving infrastructure, monitoring and retraining are built alongside the model rather than afterwards, because afterwards is when projects die.
Machine learning solutions: scope and deliverables
Feasibility first. Whether the signal you need exists in the data you have is an empirical question, and answering it takes two to three weeks rather than a full project.
Then the model, which is genuinely the smaller part. Feature engineering, validation design that avoids leakage, and honest evaluation against a baseline, frequently the baseline is a simple rule that performs almost as well, which is worth knowing.
Then the engineering: pipelines, serving, monitoring for drift, retraining triggers, and integration into the system where the decision happens.
- Feasibility assessment: does the signal exist in the data you have
- Baseline comparison so the model has to beat a simple rule to justify itself
- Feature engineering and validation design that avoids leakage
- Production serving infrastructure with latency and cost appropriate to use
- Drift monitoring on inputs and outputs with retraining triggers
- Integration into the system where the decision is actually made
When machine learning is the right tool
Problems with structured historical data, a clear outcome to predict, and enough volume that a percentage point of accuracy is worth money. Risk scoring, churn prediction, demand forecasting, anomaly detection.
It is the wrong tool when the data is unstructured text or images, where language and vision models are usually better and cheaper, and when a simple rule performs adequately, which is more often than people expect.
- Problems with structured historical data and a clear outcome to predict
- High decision volume where small accuracy gains are financially material
- Organizations with models built but never deployed
- Teams whose deployed models have degraded without anyone noticing
- Businesses where rules-based scoring has plateaued
- Companies needing to know whether ML is feasible before committing budget
Benefits of machine learning solutions
Models that actually deploy
Engineering built alongside the model rather than assumed, which is the difference between a project and a notebook.
Honest baseline comparison
Models measured against a simple rule, so you know whether the complexity is earning anything.
Feasibility answered cheaply
Two to three weeks to establish whether the signal exists, rather than finding out at month five.
Drift detected
Input and output monitoring so degradation surfaces before it shows up in business results.
Validation without leakage
Careful validation design, because leakage produces excellent test scores and useless production models.
Owned by your team
Pipelines, code and documentation handed over so the model survives the person who built it leaving.
Business challenges this solves
Models that never deployed
Good notebooks with no path to production. Engineering built alongside from the start.
Deployed models silently degrading
Accuracy falling with no alerting. Drift monitoring surfaces it before business impact.
Great test scores, poor production
Data leakage in validation. Careful design avoids the most common cause of ML disappointment.
Complexity without benefit
Models barely beating a simple rule. Baseline comparison makes that visible before deployment.
Nobody owns it after handover
Models orphaned when a data scientist leaves. Documentation and enablement built in.
Not knowing if it is feasible
Budget committed before knowing the signal exists. A short feasibility phase answers it cheaply.
Features and deliverables
Everything below is in scope on a standard engagement. Nothing here is an upsell discovered halfway through the build.
Feasibility assessment
A short empirical study establishing whether predictive signal exists in your data before a full project is funded.
Baseline benchmarking
A simple rule or heuristic built first, so the model must demonstrably beat it to justify its complexity.
Feature engineering
Feature construction from your data with careful attention to what would actually be available at prediction time.
Validation design
Temporal and group-aware validation that avoids leakage, which is the most common cause of models that fail in production.
Serving infrastructure
Batch or real-time serving sized to your latency and volume requirements, deployed on your own infrastructure.
Drift monitoring
Input distribution and output monitoring with alerting when either moves outside expected bounds.
Retraining pipeline
Automated retraining on a schedule or trigger, with validation gates before a new model replaces the old one.
Documentation and handover
Model cards, pipeline documentation and enablement so your team can operate and retrain independently.
Technologies we use for machine learning solutions
We are not tied to one vendor. Model and infrastructure choices are made on accuracy, cost per task, latency, and where your data is allowed to live.
Our AI development process
The same five stages on every engagement, so you always know what happens next and what you get at the end of it.
Discovery
We interview the people doing the work, map the workflow end to end, and audit the systems and data behind it.
AI Strategy
Every opportunity gets scored on cost to build, time to value, and annual savings, then ranked.
Pilot Build
We ship the top-ranked automation as a fixed-scope pilot so you see real output before committing further budget.
Implementation
Integration with your live systems, staff training, human-in-the-loop review gates, and a documented rollback path.
Optimization
Monthly accuracy reviews, prompt and retrieval tuning, and a written report on hours and dollars saved.
How long it takes
A typical first engagement, week by week. Complex integrations and regulated environments extend this, and we say so during discovery rather than after.
Feasibility
Data assessment and an empirical test of whether the required signal exists, with a go/no-go recommendation.
Model development
Feature engineering, baseline comparison, model training and honest validation.
Production engineering
Data pipelines, serving infrastructure, monitoring and retraining automation.
Integration
Connection into the system where the decision happens, with shadow running against current process.
Handover
Documentation, model cards, enablement sessions and 30 days of included support.
Industries we deliver machine learning solutions for
Financial Services
Document extraction, reconciliation, KYC support, and audit-ready reporting with full traceability.
Insurance
First-notice-of-loss intake, claims triage, policy Q&A, and fraud signal detection.
Manufacturing
Quality inspection, maintenance prediction, supplier communication, and production scheduling.
Retail & E-commerce
Product data enrichment, demand forecasting, support deflection, and personalized merchandising.
Logistics & Supply Chain
Document processing, carrier communication, exception handling, and inventory rebalancing.
Healthcare
Intake, prior authorization, clinical documentation, and revenue-cycle workflows built to respect HIPAA boundaries.
SaaS & Technology
AI features inside your product, support deflection, onboarding assistants, and usage analytics.
Professional Services
Proposal drafting, timesheet capture, research synthesis, and client reporting at scale.
Real-world use cases
Churn prediction
Identifying accounts at risk early enough that intervention is possible, with the drivers surfaced.
Credit and risk scoring
Risk models with explainability appropriate to regulatory requirements and adverse action notice obligations.
Demand forecasting
Item-level forecasting from sales history with seasonality and external signals incorporated.
Anomaly and fraud detection
Unusual transaction and behaviour patterns flagged for investigation with the reasoning shown.
Predictive maintenance
Equipment failure prediction where sufficient labelled failure history genuinely exists.
Lead and opportunity scoring
Conversion likelihood from historical outcomes rather than from assumed ideal-customer criteria.
Why choose DevSolutionsAI for machine learning solutions
Business case before build
Every recommendation carries an estimated cost, timeline, and annual savings figure. If the math does not work, we say so before you spend.
Vendor-neutral by design
We resell nothing and take no platform commissions. Model and infrastructure choices are made on fit, cost, and your data-residency rules.
Fixed-scope pilots
The first engagement is a defined deliverable at a defined price, not an open-ended retainer that quietly grows each quarter.
Built for handover
You own the code, the prompts, the infrastructure, and the documentation. No lock-in to a proprietary wrapper you cannot leave.
Human-in-the-loop where it counts
Anything customer-facing, clinical, financial, or legal gets a review gate, a confidence threshold, and a logged audit trail.
Security reviewed early
Data flow diagrams, retention rules, and access boundaries are agreed in week one, not retrofitted after your security team objects.
Find out what machine learning solutions would cost you, before you commit to anything
Every engagement is quoted after a short discovery, so you get a fixed written price built around your actual volumes rather than a rate card that assumes someone else’s business.
The first call is thirty minutes and free. Bring one workflow. We will tell you what it is likely costing you each year, roughly what automating it would take, and whether we think it is worth doing at all.
- A written savings estimate before any paid work
- Fixed scope and fixed price, agreed up front
- Full ownership of everything we build for you
- An honest recommendation when the numbers do not work
Figures are internal measurements across recent engagements, reported to every client monthly in writing.
Illustrative project scenario
A model that beat the rules by enough to justify itself, and not by more
Challenge. A leasing company scored applications with a rules-based system built over a decade. They wanted machine learning, expecting a substantial improvement. Nobody had established what the existing rules actually achieved as a baseline.
What we built. A feasibility phase first, which established the rules baseline properly and then tested achievable model performance. The model improved default prediction meaningfully but less dramatically than expected. We reported that honestly, including the finding that roughly half the improvement came from three features the rules did not use, which could have been added to the rules directly.
Outcome. The client proceeded with the model, but with realistic expectations and a clear understanding of where the gain came from. Explainability was built in for adverse action notice requirements. The model was deployed with shadow running against the rules for two months before it took over decisions.
Illustrative project scenario. The figures demonstrate how a project could be scoped and evaluated; they are not verified client results or an audited average.
What clients say about working with us
Machine Learning Solutions FAQs
Why do so many machine learning projects fail to deploy?
Because the model is the smaller part of the work and frequently the only part that was scoped. Production requires live data pipelines, serving infrastructure, monitoring, retraining, and integration into the system where a decision actually happens. That engineering is rarely a data scientist’s specialism, so the model stays in a notebook. We build the engineering alongside the model rather than afterwards.
How do we know if machine learning will work for our problem?
A two to three week feasibility study answers it empirically. We test whether predictive signal exists in your data, establish what a simple rule achieves as a baseline, and estimate achievable accuracy. It is far cheaper to find out that the signal is not there in week three than in month five, and we do recommend against proceeding on a meaningful share of these studies.
Why do you build a simple rule first?
Because it is frequently competitive, and you should know that before committing to a model with ongoing retraining and monitoring costs. On a recent risk scoring engagement the model beat the rules baseline, but half the improvement came from three features the rules simply did not use, which could have been added directly for a fraction of the cost. That is worth knowing.
What is data leakage and why does it matter?
Leakage is when information that would not be available at prediction time leaks into training data, producing excellent test scores and a model that fails in production. It is the single most common cause of ML disappointment and it is subtle, a feature computed after the outcome, or a temporal split that lets the model see the future. Careful validation design is unglamorous and it is what separates models that work from models that scored well.
What happens when the model degrades?
Drift monitoring watches both input distributions and output patterns, alerting when either moves outside expected bounds. Retraining runs on a schedule or on a drift trigger, with validation gates so a worse model cannot silently replace a better one. Models degrade as the world changes; the failure is not degradation but not noticing it.
How much does machine learning cost?
A feasibility study is $14,000 to $25,000 over two to three weeks. A production model including serving, monitoring, retraining and integration typically runs $70,000 to $180,000 depending on data complexity and integration requirements. Ongoing operations run monthly. We would rather sell you the feasibility study and then recommend against the build than deliver a model on insufficient signal.
Services that pair well with this one
Most clients combine two or three of these. We will tell you the right sequence during discovery.
Ready to scope your machine learning solutions project?
Book a free 30-minute consultation. Bring one workflow and leave with a realistic estimate of what it would cost to automate and what it would save.