── AI product engineering services ──

AI products, built to launch.

AI-first products and features designed, built and launched end to end: product strategy, UX, models and engineering in one team, with the evaluation that keeps quality up after launch.

Start a project →

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

Product, UX and ML · Evaluation-first · A/B tested · You own the code

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 · AI product engineering services ──

Six services, one product team.

AI product engineering is designing, building and running software where AI is central to the value, with product, UX and machine learning in one team.

  1. 01

    AI product strategy.

    Which AI features your users will pay for, and the smallest version worth building first.

    Deliverable · Product brief and roadmap
  2. 02

    AI product development.

    Front end, back end and AI built together by one team, from prototype to launch.

    Deliverable · Launched product or feature
  3. 03

    AI product integration.

    AI features added to your existing product without a rewrite.

    Deliverable · Shipped feature
  4. 04

    Automation and optimisation.

    The workflows behind the product automated, so it scales without adding headcount.

    Deliverable · Automated operations
  5. 05

    Security and compliance.

    Security reviews, privacy design and compliance documentation from sprint one.

    Deliverable · Security and compliance pack
  6. 06

    AI product management.

    Roadmaps, experiments and metrics run by product managers who understand models.

    Deliverable · Product operating rhythm
── 02 · Anatomy of an AI product ──

Six layers, built as one.

An AI product is more than a model with a UI. Each layer below has to work, and be tested, for the product to hold up.

ExperienceWhat users see and controlStreaming UISuggestions and undoExplanationsFeedback buttons
Product logicRules, workflows and pricingFeature flagsEntitlementsUsage limitsWorkflows
AI layerModels, retrieval and agentsLanguage modelsRetrievalAgentsClassic ML
DataWhat the AI learns from and readsEvent trackingFeature storeVector indexLabelled sets
PlatformWhere it runsYour cloudModel servingCI/CDObservability
TrustWhat keeps it safeEvaluation gatesGuardrailsAccess controlAudit logs
── 03 · AI vs traditional software ──

What AI products need that software doesn't.

The same engineering discipline, plus a few habits that only matter when the core of the product is probabilistic.

Traditional softwareAI product
TestingPass-or-fail unit testsEvaluation sets scored on every change
ReleasesShip when the code is doneShip when the quality bar is met
User experienceDeterministic screensSuggestions, confidence, undo and feedback
Running costMostly fixedGrows with usage; needs budgets
After launchFix bugsMonitor drift, retrain and re-evaluate
── 04 · Experimentation ──

Every launch is an experiment.

New AI features reach a slice of users first. They roll out further only when the product metrics, not just the model metrics, say so.

  • Feature flags on every AI feature, with an off switch
  • A/B tests on the product metric that matters
  • Guardrail metrics for cost, latency and revenue
  • Model evaluation and product analytics on one dashboard
— A/B test · 14 days · 48,000 sessions —
Control · keyword search3.10% conversion
Variant · AI query suggestions3.29% conversion
Revenue per visit+1.1% · no drop
Search latency · p95310ms · in budget
AI cost per 1k searches$0.40
Illustrative sample results.
── 05 · Security and compliance ──

Compliance built into every layer.

Security reviews and compliance evidence are produced as the product is built, not reconstructed before an audit.

  1. 01

    Secure by design.

    Threat modelling in sprint one, with prompt-injection and data-leak risks on the list.

  2. 02

    Privacy by default.

    Personal data is minimised, masked before it reaches a model, and deleted on schedule.

  3. 03

    Evaluation gates.

    A release that drops below the quality bar on its evaluation set doesn't ship.

  4. 04

    Access and audit.

    SSO, role-based access and audit logs on every product surface and admin tool.

  5. 05

    Compliance evidence.

    Documentation built to support SOC 2, GDPR and HIPAA reviews, with your counsel signing 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 ──

AI products for your industry.

Every industry has its own users and rules. Product discipline, evaluation and experiments stay the same.

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 product brief to launch.

A clickable prototype and an evaluation set come before the build. Working software every Friday after that.

  1. 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.

  2. 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.

  3. 03 Build · sprint 1

    Architecture and prototype.

    Model choice, retrieval design and integration plan, proven against the eval set.

  4. 04 Build · every Friday

    Build in the open.

    Your repository, your cloud. A weekly demo with accuracy, latency and cost on one screen.

  5. 05 Run · go-live

    Deploy.

    A staged rollout behind a feature flag, with runbooks, monitoring and a rollback path.

  6. 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 product stack we build on.

Modern web and mobile frameworks, any model, and the infrastructure to run both.

— 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 ──

Product 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 is AI product development different from traditional software?

Quality is measured, not just tested: every change is scored on an evaluation set, releases wait for the quality bar, and the product is monitored for drift after launch. The UX also has to handle uncertainty with suggestions, confidence and undo.

What does it cost to build an AI product?

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.

What is the typical timeline?

After a two-week discovery sprint, a first release of an AI feature usually takes six to twelve weeks. A new AI-first product typically reaches its first launch in three to six months, depending on scope.

Do you build the whole product or just the AI?

Either. We can build the full product with design, front end, back end and AI in one team, or add AI features to a product your team already runs.

How do you decide which AI features to build?

We start from the user problem and the metric it should move, prototype quickly, and test with real users before committing engineering time.

Do you maintain the product after launch?

Yes. We monitor quality, drift and cost, run experiments and upgrade models against your evaluation set, and hand over to your team whenever you're ready.

Who owns the code and the models?

You do. Everything is built in your repositories and cloud accounts, and trained models and evaluation sets are yours.

Can AI features run in mobile apps?

Yes. We build AI features for iOS and Android, running models on the device where speed or privacy require it and in the cloud where they don't.

── 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.

  1. 01Scoping call30 minutes with an engineer, not a salesperson.
  2. 02Written estimateAn evaluation plan, a timeline and a price within 5 working days.
  3. 03KickoffSign the scope and the discovery sprint starts.

Prefer to message directly? WhatsApp +91 7096010005

— Start a pilot —

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.

Fixed fee. Two weeks. Starts with a 30-minute call.

Start a pilot →