AI and data · Machine learning
Machine learning development services for forecasting, scoring and recommendations
We build machine learning and predictive analytics systems that forecast demand, score leads and risk, recommend products and content, and catch anomalies, then keep them accurate with training pipelines, drift monitoring and retraining. Not every problem needs a large language model; many are better solved by a gradient-boosted model trained on your own data.
Short answer
Machine learning development means building models that learn from your data to forecast, score, rank, recommend or detect anomalies, plus the pipelines that keep them accurate. Innovation Insight builds predictive analytics on scikit-learn, XGBoost and PyTorch, or managed platforms like SageMaker AI, Vertex AI and BigQuery ML. Discovery with a data readiness review costs $3k to $8k, an ML feature in an existing product $8k to $60k, and an ML-driven MVP $25k to $60k.
Reviewed by Zain Khalid Malik, CTO & Co-founder · Updated
What we build
| Problem | What the model predicts | Typical inputs | Typical models |
|---|---|---|---|
| Demand and sales forecasting | Orders, bookings or stock needed per product, location and week | Sales history, calendar, prices, promotions, weather | Gradient boosting with lag features, Prophet-style models, BigQuery ML ARIMA_PLUS |
| Lead and account scoring | Likelihood to buy, intent, fit with your ideal customer profile | CRM history, engagement signals, firmographics | Logistic regression, gradient boosting, ranking models |
| Churn and risk scoring | Which customers or accounts are likely to leave, default or need attention | Usage, payments, support tickets, tenure | Gradient boosting with calibrated probabilities |
| Recommendations | What to show each user next: products, events, tracks, posts | Views, purchases, ratings, item attributes, embeddings | Collaborative filtering, content embeddings, learning-to-rank |
| Feed ranking | Order of items in a personalised feed | Recency, follows, engagement, item scores | Scoring functions first, then learned ranking |
| Anomaly and fraud detection | Transactions, sensor readings or log events that look wrong | Event streams, historical baselines | Isolation forests, autoencoders, rules plus models |
Classic machine learning or a large language model?
Large language models are good with text and poor with tables of numbers. For forecasting, scoring and ranking on structured data, a classic model trained on your history is usually more accurate, cheaper per prediction by orders of magnitude, faster and easier to explain to a finance or risk team.
| Task | Better fit | Why |
|---|---|---|
| Forecast next month's demand per store | Classic ML | Numeric history, seasonality and features; an LLM adds cost and no accuracy |
| Score 50,000 leads every night | Classic ML | Milliseconds per prediction, calibrated probabilities, explainable features |
| Recommend products from purchase history | Classic ML with embeddings | Collaborative filtering and ranking models are built for this |
| Flag unusual transactions | Classic ML plus rules | Fast, auditable, tunable false-alarm rate |
| Explain why an account's score changed, in a weekly email | LLM on top of the model's output | Language generation from structured facts |
| Classify free-text support tickets | LLM or a small fine-tuned text model | Text understanding with little labelled data |
| Extract fields from contracts | LLM | See AI document processing |
Many good systems use both. On TamTracker, intent signals from nine integrations are computed with delta-based maths, while GPT-4 writes the weekly intelligence emails and ranks suggested accounts. For text-first problems, our generative AI development page is the better starting point.
Data readiness checklist
Before we quote a model, we check the data. Most of the risk in a machine learning project sits here.
- Target defined: what exactly are we predicting, over what horizon, and how will a prediction change a decision?
- History: enough examples of the outcome. For a churn model that usually means hundreds of churned customers, not a dozen.
- Labels you trust: outcomes recorded consistently, with the same definition across the whole period.
- No leakage: every feature must be known at the moment the prediction is made. Leakage produces models that look brilliant offline and fail in production.
- Access and joins: the data can be extracted on a schedule and joined on stable keys across systems.
- Baseline: a simple rule or last-year number to beat. If the model cannot beat it, we say so.
- Privacy: personal data mapped, lawful basis confirmed, and features that act as proxies for protected characteristics reviewed.
Build on open source or a managed platform
| Option | Strengths | Trade-offs | When we use it |
|---|---|---|---|
| Python (scikit-learn, XGBoost, LightGBM, PyTorch) served by FastAPI | Full control, no platform fees, runs on any cloud | You own the pipelines, registry and monitoring | Most first models, cost-sensitive teams, multi-cloud |
| Amazon SageMaker AI | Managed training, model registry, endpoints and monitoring on AWS | Instance-hour pricing; endpoints cost money while idle unless you use serverless or batch | Teams already on AWS with several models in production |
| Google Vertex AI | Managed pipelines, feature store, endpoints and AutoML | Similar instance-hour model; Google Cloud lock-in | Teams on Google Cloud, or pairing with Gemini |
| BigQuery ML | Train and predict with SQL where the data already lives | Fewer model types and less control over features | Analytics teams, forecasting and classification on warehouse data |
We usually start with open-source models served by a small Python and FastAPI service, and move to a managed platform when the number of models or the team size makes it worth the fees.
MLOps: keeping models accurate after launch
A model is a snapshot of the past. Prices change, customers change, a new product launches, and accuracy decays without anyone noticing. The paper "Hidden Technical Debt in Machine Learning Systems" (NeurIPS 2015) put it well: the model code is a small part of a production ML system. We build the rest.
- Training pipelines: data extraction, feature building, training and evaluation as code, run on a schedule or on demand, with every run versioned.
- Feature store or shared feature code: the same feature definitions for training and serving, so the model sees in production what it saw in training.
- Model registry: every model with its data snapshot, metrics and approver, and one-click rollback.
- Offline replay: a test run of the candidate model over historical traffic before it ships, checking the distribution of its outputs as well as its average score.
- Shadow and A/B releases: the new model runs beside the old one, then takes a share of traffic, with business metrics compared.
- Drift monitoring: alerts when input distributions, prediction distributions or measured accuracy move outside agreed bounds.
- Retraining: scheduled or triggered by drift, with the evaluation gate deciding whether the new model replaces the old one.
Explainability and fairness
A score nobody can explain is a score nobody uses. Sales teams ignore lead scores they do not trust, and risk teams cannot sign off a model they cannot audit. We build explanation in from the start.
- Feature contributions per prediction (SHAP values for tree models), shown as the top three reasons next to each score in the user's screen.
- Calibrated probabilities, so a score of 0.7 means roughly seven in ten, and thresholds can be set from business costs rather than guessed.
- Segment reports: accuracy broken down by region, customer size, channel and any group where unequal errors would matter.
- Reviews of features that could act as proxies for protected characteristics, such as postcode, before they go into credit, pricing or hiring models.
- A plain-language model card for each model: what it predicts, what data it uses, known limits and who approved it.
Mistakes we have seen in ML projects
| Mistake | Symptom | Fix |
|---|---|---|
| Random train and test split on time-ordered data | Great offline score, poor live results | Split by time: train on the past, test on the most recent period |
| Optimising the average metric only | A recommender that returns the same item to everyone | Check output distributions, coverage and diversity as well as accuracy |
| Features computed differently in training and serving | Accuracy drops on day one with no obvious cause | One feature definition shared by the training pipeline and the API |
| No baseline | Months spent on a model that a simple rule would have matched | Build the rule or last-period baseline first and report against it |
| Shipping without monitoring | Accuracy decays for months before anyone notices | Drift alerts and a monthly accuracy review from launch |
What machine learning development costs
| Engagement | Cost | Timeline |
|---|---|---|
| Discovery: data readiness review, baseline, feasibility model, fixed estimate | $3k to $8k | 1 to 3 weeks |
| ML feature in an existing product (one model, pipeline, monitoring) | $8k to $60k | 4 to 12 weeks |
| ML-driven MVP (new product built around predictions or recommendations) | $25k to $60k | 10 to 14 weeks |
| Ongoing retraining, monitoring and improvement | $1.5k to $8k per month | Monthly |
Cloud compute for training and serving comes on top and is usually modest for tabular models. The machine learning app development cost guide shows the breakdown, the predictive analytics guide explains where predictions pay back, and pricing lists every rate. If you want engineers inside your own team instead, hire AI and ML engineers or data engineers.
How a project runs
- Discovery ($3k to $8k): define the target and the decision it drives, audit the data, build a baseline and a quick feasibility model so you see real numbers before committing.
- Data pipeline: reliable extraction and feature building on a schedule, with tests on data quality.
- Model development: several candidate models compared on a time-based hold-out set against the baseline, with error analysis by segment.
- Integration: predictions served by API or written back to your database, CRM or warehouse, with the user-facing feature built around them.
- Release: offline replay, shadow mode, then a staged rollout with business metrics tracked.
- Operate: drift monitoring, scheduled retraining and a monthly review of accuracy and impact.
Machine learning we have shipped
- Paint Nite: a painting recommendation engine for a live events platform in the US and Canada, tuned over four measured iterations with an offline replay harness that caught a degenerate result (one artwork picked 187 times out of 187) before production.
- Cuts Like A Knife: audio similarity search with OpenL3 embeddings indexed in FAISS and Amazon OpenSearch, a background processor that embeds new uploads automatically, and collaborative filtering for recommendations, served from a Python FastAPI machine learning service.
- TamTracker: a B2B SaaS that scores target accounts on buying intent from nine sources including LinkedIn Ads, HubSpot and Google Ads, with an ICP-driven suggestion engine using lookalike detection and AI ranking.
- Score Hub: a personalised social feed for a football prediction platform, ranked by a custom scoring algorithm and served from Redis with surgical cache invalidation on match updates.
For image and video problems, see computer vision development. The full range of AI work is on our AI and data services page.
Sources and further reading
Next step
Tell us what you're building and get a written estimate.
A senior engineer replies within one business day. NDA on request.
Products we've shipped, and what happened next.
Case studies written from the technical documentation of each project: the stack, the scale and the outcome.
Questions we get asked a lot.
How much does machine learning development cost?
Discovery with a data readiness review and a feasibility model costs $3k to $8k. A machine learning feature in an existing product costs $8k to $60k, and an ML-driven MVP $25k to $60k. Retraining and monitoring run $1.5k to $8k per month.
What is predictive analytics?
Using historical data and statistical or machine learning models to estimate future outcomes, such as demand, churn, conversion or risk, so a person or a system can act before it happens.
Do we need a large language model for this?
Usually not for numbers. Forecasting, scoring, ranking and anomaly detection on structured data are better served by classic models. LLMs help with text: explaining results, classifying tickets or extracting fields.
How much data do we need?
Enough examples of the outcome you want to predict, recorded consistently. For a churn or conversion model that is typically hundreds of positive cases. We check this in discovery and tell you if a simple rule will do.
Should we use SageMaker, Vertex AI or build our own?
Start with open-source models and a small serving API unless you already run several models. Move to SageMaker AI or Vertex AI when managed pipelines, registry and monitoring save more than they cost. BigQuery ML suits analytics teams on BigQuery.
How do you know the model is still working after launch?
Drift monitoring on inputs and predictions, measured accuracy once outcomes arrive, and alerts when either moves outside agreed bounds. Retraining is scheduled and gated by evaluation.