── Machine learning development services ──

Better than your baseline.

Forecasting, scoring, recommendation and deep learning models trained on your data, measured against what you use today, and run with MLOps so they stay accurate.

Start a project →

Free 30-minute scoping call. Written estimate within 5 working days.

Your data · Your cloud · Explainable · Retrained on drift

Illustrative interface. Values are sample data.

0+.AI systems in production

0.Days to first deploy · median

0.0%.Uptime · managed inference · 12 mo

0.0×.Cost reduction · fine-tuned vs API · median

* Sample figures for layout review. Replace with audited numbers from engagement reports before launch.

── 01 · Machine learning development services ──

Eight services, one model lifecycle.

Machine learning development is building models that learn patterns from your data to predict, rank or detect, and running them reliably in production.

01
ML consulting.
Which problems suit ML, what data they need, and the baseline to beat.
Deliverable · ML opportunity brief
02
Custom model development.
Forecasting, classification, ranking and anomaly models trained on your data.
Deliverable · Production model
03
Deep learning.
Vision, language and sequence models when simpler methods reach their limit.
Deliverable · Deep learning model
04
ML engineering.
Feature pipelines, training code and serving built like production software.
Deliverable · Feature pipelines
05
Integration and deployment.
Models behind APIs or inside your data platform, wired into the systems that act on them.
Deliverable · Deployed model service
06
MLOps.
Automated training, evaluation, deployment and monitoring, with rollback.
Deliverable · MLOps pipeline
07
Fairness and explainability.
Bias audits across groups and reason codes for every prediction that matters.
Deliverable · Fairness report
08
Managed ML.
We run and retrain your models in your cloud, with a monthly review.
Deliverable · Managed model service

── 02 · What machine learning solves ──

Six problems ML solves well.

Each starts with a number your team already tracks, and a baseline the model has to beat.

Forecasting.

Demand, inventory, staffing and cash forecasts, with error bands planners can act on.

Typical use · demand, stock, cash

Classification and scoring.

Churn, credit, fraud and lead scores, with the reasons behind each score.

Typical use · churn, credit, leads

Recommendations.

Products, content or next best actions ranked for each user, tested against your current rules.

Typical use · retail, media, B2B

Anomaly detection.

Unusual transactions, sensor readings or usage spotted early, with a reason for the reviewer.

Typical use · fraud, equipment, security

Optimisation.

Routes, schedules, prices and allocations chosen to hit a target within your constraints.

Typical use · logistics, pricing, rostering

{
  "label": "refund",
  "urgency": "high",
  "confidence": 0.94
}

Text classification.

Tickets, emails and documents tagged and routed by intent, topic or urgency.

Typical use · support, claims, compliance

* Previews show illustrative sample outputs.

── 03 · Model lifecycle ──

A model is never finished.

Data shifts, customers change and accuracy drifts. Every model we ship runs this loop, owned by a named team.

Data scientists
Modelling
Frame the problem, pick the method and prove it beats the baseline.
ML engineers
Production code
Turn notebooks into tested training and serving code.
Data engineers
Pipelines
Build reliable feature pipelines from your sources.
MLOps engineers
Automation
Automate training, deployment, monitoring and rollback.
Solution architects
Design
Fit the model into your systems, security and cloud.
Governance specialists
Risk
Run fairness audits and keep model documentation current.

── 04 · Choosing a method ──

The simplest model that works.

We start with the method that is cheapest to run and easiest to explain, and move on only when your evaluation says so.

Method	Best for	Data needed	Explainability	Serving cost
Rules and baselines	A first benchmark	None	Complete	Minimal
Gradient-boosted trees	Tabular data: scoring, churn, pricing	Thousands of labelled rows	High, with reason codes	Low
Time-series models	Forecasts with seasonality	Two or more years of history	Medium	Low
Deep learning	Images, audio, text, complex sequences	Large labelled sets	Lower; needs tooling	Medium to high
Fine-tuned language models	Text tasks at high volume	Hundreds to thousands of examples	Lower; needs evaluation	Medium

── 05 · Responsible ML ──

Accurate, fair and explainable.

A model that is right on average can still be wrong for a group. These checks run before launch and every month after.

01
Fairness audits.
Error rates compared across customer groups before launch, against a threshold agreed in writing.
02
Reason codes.
Every high-stakes prediction comes with the factors that drove it, for staff and for customers who ask.
03
Drift monitoring.
Input and prediction drift tracked daily; crossing a threshold triggers review or retraining.
04
Reproducible training.
Data versions, code and parameters are logged, so any model can be rebuilt and audited.
05
Compliance support.
Documentation built to support GDPR, fair-lending and model-risk rules, with your counsel signing off.

/ fairness report · credit-score v4Pre-release

1.03.approval ratio · limit 0.80–1.25

Group	Approval	False neg.	Δ
Group A	61%	7.2%	—
Group B	59%	7.9%	+0.7
Group C	62%	6.8%	−0.4

Copilot: all groups within the agreed thresholds. Cleared for release.DetailsSign off

Illustrative interface. Values are sample data.

— Case studies —

The first cohort is being written.

Named case studies publish here with the client's sign-off, real numbers and the honest limits. Want to be one of them?

Talk to us →

── 06 · Industries ──

Machine learning for your industry.

Data and regulation differ by industry. Baselines, evaluation and monitoring work the same way.

Mobility.

Dispatch, ETA and rider-support agents for ride-hailing and fleet platforms.

ETA models · Demand forecasting · Support agents

Logistics.

Route optimisation, document extraction and exception handling across the shipment lifecycle.

Route optimisation · Document AI · Exception triage

Finance.

KYC document extraction, fraud signals and customer assistants that stay inside compliance rules.

KYC extraction · Fraud signals · Compliance copilots

Real estate.

Listing enrichment, valuation models and lead-qualifying agents for brokerages and proptech.

Valuation · Listing enrichment · Lead agents

Retail / Manufacturing.

Demand forecasting, visual inspection and inventory copilots on the shop and factory floor.

Forecasting · Visual inspection · Inventory copilots

eCommerce / Consumer goods.

Product search, personalisation and post-purchase support agents that cite the order.

Semantic search · Personalisation · Support agents

Travel.

Itinerary assistants, dynamic pricing and disruption-handling agents for OTAs and operators.

Itinerary agents · Pricing · Disruption handling

— Not listed? —

Let's discuss your industry.

If the data is messy and the stakes are real, the method still applies. Bring the workflow; we'll bring the evaluation set.

Start a project →

── 07 · Development process ──

From baseline to production model.

The baseline and the evaluation set come first. A working demo every Friday once the build starts.

01 Discovery · week 1
Workflow and data audit.

We map the process, pull a sample of real data, and check what is usable, missing or sensitive.
02 Discovery · week 2
Evaluation set.

We build the test set from your real examples, so “done” has a number before any product code exists.
03 Build · sprint 1
Architecture and prototype.

Model choice, retrieval design and integration plan, proven against the eval set.
04 Build · every Friday
Build in the open.

Your repository, your cloud. A weekly demo with accuracy, latency and cost on one screen.
05 Run · go-live
Deploy.

A staged rollout behind a feature flag, with runbooks, monitoring and a rollback path.
06 Run · monthly
Monitor and improve.

Drift alerts, model upgrades run through your evals, and a monthly cost and quality review.

── 08 · Technology stack ──

The ML stack we build on.

Training, serving and monitoring tools chosen to fit your cloud and your team.

— Models —

OpenAI
Anthropic
Gemini
Llama
Mistral
Qwen

— Agents & orchestration —

LangChain
Temporal
FastAPI
Python

— Retrieval & data —

pgvector
Pinecone
Elasticsearch
Redis
Databricks
Snowflake

— Training & serving —

PyTorch
Hugging Face
vLLM
NVIDIA TensorRT
ONNX
OpenCV

— MLOps & observability —

MLflow
Weights & Biases
Grafana
Prometheus
OpenTelemetry

— Cloud & infrastructure —

AWS
Google Cloud
Azure
Kubernetes
Terraform
Docker

* Tools we build with. No vendor partnership or endorsement is implied.

── 09 · FAQ ──

ML questions, answered.

Short answers here. Longer ones on the scoping call.

— Still have a question? —

Message us on WhatsApp. An engineer replies within one working day.

How much does it cost to develop a machine learning model?

Projects start with a fixed-fee discovery sprint, quoted on the scoping call. The build is priced per milestone, and every quote is written and fixed for its scope.

How long does it take to build an ML model?

After a two-week discovery sprint, a first production model usually takes six to twelve weeks, depending on data readiness and integration. A prototype scored against your baseline often arrives in the first few weeks.

What data do we need?

Enough historical examples of the outcome you want to predict, with the signals that come before it. Discovery measures what you have and says plainly if more labelling or history is needed.

How do you make sure a model is fair?

We measure error and approval rates across relevant groups before launch, agree thresholds in writing, and repeat the check every month in production.

Can you explain why a model made a prediction?

Yes. Where decisions matter, each prediction comes with reason codes showing the factors that drove it, and we prefer easier-to-explain model types when the accuracy cost is small.

Do you support models after launch?

Yes. We monitor accuracy and drift, retrain on a schedule or when drift is detected, and review quality and cost with you every month, or hand over with runbooks if your team takes over.

Can ML work with our existing systems?

Yes. Models are served behind an API or run inside your data platform, and results are written back to the CRM, ERP or dashboards your teams already use.

When is machine learning not the right choice?

When a simple rule already works, when there is too little history to learn from, or when nobody will act on the prediction. We say so in discovery.

── Start a project ──

Bring the workflow. We'll bring the plan.

Three steps from first message to kickoff. No deck, no commitment until you sign.

01Scoping call30 minutes with an engineer, not a salesperson.
02Written estimateAn evaluation plan, a timeline and a price within 5 working days.
03KickoffSign the scope and the discovery sprint starts.

Prefer to message directly? WhatsApp +91 7096010005

— Start a pilot —

Your name

Business name

Industry (optional)

What job eats the most time right now?

This is the one that decides whether a pilot is worth doing.

Nothing is stored on this website — the form opens WhatsApp with the message written out, including the pages you looked at, and you press send.

Better than your baseline.

By the numbers

Eight services, one model lifecycle.

ML consulting.

Custom model development.

Deep learning.

ML engineering.

Integration and deployment.

MLOps.

Fairness and explainability.

Managed ML.

Six problems ML solves well.

Forecasting.

Classification and scoring.

Recommendations.

Anomaly detection.

Optimisation.

Text classification.

A model is never finished.

Data scientists

ML engineers

Data engineers

MLOps engineers

Solution architects

Governance specialists

The simplest model that works.

Accurate, fair and explainable.

Fairness audits.

Reason codes.

Drift monitoring.

Reproducible training.

Compliance support.