15+
Years of Experience
AI Data Product Development Company
AI-first engineering for data products: retrieval-augmented assistants, prediction APIs, feature pipelines and analytics copilots built by a team with 120+ AI products in production, so your data stops sitting in dashboards and starts making decisions.
- 2010
- Operating since
- 9,000+
- Projects delivered
- 3,900+
- Apps published
- 120+
- AI products shipped
Trusted by the brands we build for
120+
AI Products in Production
98%
Model Accuracy Achieved
9,000+
Projects Delivered
AI data products we build
From raw data estate to a product users rely on daily.
RAG knowledge assistants
Assistants grounded in your documents, tickets, and databases with cited answers. Hallucination is an engineering problem, so retrieval quality, citation UI, and refusal behavior are designed in from the first sprint.
- ✓Document & Database Connectors
- ✓Retrieval Quality Evaluation
- ✓Cited Answer Rendering
- ✓Refusal & Confidence Thresholds
- ✓Access-Control-Aware Search
Prediction APIs
Forecasting and scoring models served behind versioned, monitored endpoints. Every prediction is logged with its feature inputs, so a wrong answer can be investigated instead of shrugged at.
- ✓Feature Store & Training Pipelines
- ✓Model Versioning & Rollback
- ✓Latency-Budgeted Serving
- ✓Drift Detection & Retraining Triggers
- ✓Prediction Audit Logs
Data pipelines & warehouses
Ingestion, transformation, and quality checks that make analytics trustworthy. Deduplication, idempotent loads, and freshness SLAs are the difference between a single source of truth and three arguing ones.
- ✓Batch & Streaming Ingestion
- ✓Transformation Orchestration
- ✓Data Quality & Freshness SLAs
- ✓Lineage & Catalog Tooling
- ✓Cost Monitoring per Pipeline
Analytics copilots
Natural-language querying over governed data with generated SQL you can inspect. Analysts approve queries instead of writing them from scratch, and every query runs inside existing permission rules.
- ✓Text-to-SQL With Schema Guardrails
- ✓Governed Metric Definitions
- ✓Query Approval Workflows
- ✓Chart & Report Generation
Intelligent document processing
Classification, extraction, and validation for invoices, claims, and contracts. Humans review the low-confidence cases, which is where the accuracy actually comes from.
- ✓OCR & Layout-Aware Extraction
- ✓Confidence-Based Human Review
- ✓Field-Level Validation Rules
- ✓Structured Output to Your Systems
MLOps & model governance
CI/CD for models, evaluation harnesses, and audit trails. Models iterate weekly without breaking the record of what ran, on what data, with what result.
- ✓Evaluation & Regression Harnesses
- ✓Staged Model Rollouts
- ✓Training Data Versioning
- ✓Bias & Performance Reporting
A complete AI data stack, layer by layer
Data foundation, model layer, and product surface, each engineered to ship.
Data platform layer
The foundation nobody sees and everybody depends on. We build ingestion, storage, and quality controls sized to your volume, because a model is never better than the data feeding it.
- ✓Streaming & Batch Connectors
- ✓Warehouse & Lakehouse Design
- ✓Data Quality Test Suites
- ✓PII Detection & Masking
- ✓Lineage Tracking
- ✓Access Policies per Domain
Model & AI layer
Where domain knowledge becomes a system. Model selection, fine-tuning, and evaluation are run against your data and your definition of 'correct', not a generic benchmark.
- ✓Fine-Tuning & Prompt Engineering
- ✓RAG Architecture & Tuning
- ✓Evaluation Datasets Built With You
- ✓A/B Testing Infrastructure
- ✓Drift & Retraining Automation
Product & console layer
The surface users touch and the controls your team runs. Assistant UIs, review queues, and admin consoles ship in the same build, so the product is operable on day one.
- ✓Assistant & Copilot UIs
- ✓Human Review Queues
- ✓Usage & Cost Dashboards
- ✓Feedback Capture Loops
- ✓Model Performance Consoles
- ✓Role-Based Admin Controls
revenue model
How your AI data product earns
AI data products pay back through four admin-configurable levers: seat-based access to assistants, usage-based API billing for prediction endpoints, workflow automation that removes manual processing hours, and premium analytics tiers sold to your own customers. Because every lever is metered inside the platform, you can run per-customer pricing experiments without engineering involvement.
Internally, the payback shows up in cycle time. Support assistants cut handle time, extraction pipelines cut manual keying, and forecast models cut stockouts and overproduction. We baseline the metric you name in discovery and report against it at handover, so the ROI conversation is grounded in your numbers, not our slides.
Every build ships with
Standard on every AI data product we deliver.
100% Source-code ownership
Models, pipelines, prompts, and evaluation harnesses transfer to you. No vendor lock-in, no per-query royalties, no black boxes you cannot inspect.
Evaluation, not vibes
Every AI feature ships with a measured accuracy figure against a held-out test set you helped build, so 'it works' has a number behind it.
Security & privacy by design
PII masking, access-aware retrieval, encrypted storage, and audit trails as standard, aligned with your regulatory scope.
Support beyond handover
60 days of dedicated post-launch support and 12 months of updates included. Ready-made foundations can deploy on your branding in 6 working days.
AI projects that shipped and stayed shipped
Marvel Studios
A fan streaming experience with trailers, behind-the-scenes content, and global premiere events. We built high-concurrency streaming infrastructure that held up during worldwide release drops. That engineering carried 10M+ streams to a 65% completion rate.
10M+
Streams
65%
Completion Rate
4.9/5
App Rating
Heineken
A brand entertainment hub distributing video campaigns to global audiences. We engineered the CDN-backed delivery layer and the campaign analytics that track views through to conversion. 25M+ video views and a 2.2X lift in watch time followed.
25M+
Video Views
2.2X
Watch Time
18%
Conversion Lift
Tipsyryde
An alcohol delivery super app with real-time order tracking, live driver updates, and age-verification gating at checkout. We engineered the dispatch logic, payment flows, and compliance checks across the driver and customer apps. It has since passed 1.5M+ downloads with an 18-minute average pickup time.
1.5M+
Downloads
18 min
Avg Pickup Time
4.8/5
App Rating
How an AI data product build runs
From data audit to a model in production, on a written plan.
- 01
Discovery & data audit
We inventory your data sources, quality, and access rights, then return a feasibility verdict on the AI use case before you spend on build.
✓ step 01 complete - 02
Evaluation design
We define what 'accurate' means for your product and build the test set first, because a model without an evaluation harness cannot be improved safely.
✓ step 02 complete - 03
Agile build sprints
Two-week sprints with demo builds on your real data, and accuracy metrics reported every increment.
✓ step 03 complete - 04
Pilot & Red-teaming
Controlled rollout with adversarial testing, cost monitoring, and human review flows tuned before general release.
✓ step 04 complete - 05
Launch & iterate
Production launch with drift monitoring, 60 days of post-launch support and 12 months of updates.
✓ step 05 complete
The stack behind our AI data products
Chosen for reliability under real traffic, not demo-day shine.
LLM & RAG tooling
Open-weight and frontier models with retrieval pipelines tuned per domain, plus evaluation harnesses from our machine learning services practice.
Data infrastructure
Spark, dbt-style transformations, Kafka streaming and columnar warehouses sized to your volume.
Serving & mLOps
Containerized inference, feature stores, model registries and staged rollouts with rollback.
Backend & APIs
Python and Node.js services with versioned, latency-budgeted prediction endpoints.
Product frontends
React and Next.js consoles, mobile copilots on Swift, Kotlin and Flutter.
Cloud & security
AWS, Azure and GCP deployments with PII masking, encryption and audit trails built in.
/09
Why choose Moxace for AI data product development
We have shipped 120+ AI products into production, which teaches you what demos never do.
Accuracy you can audit
Every model ships with evaluation results, a test set, and a documented failure profile, so trust in the system is earned with evidence rather than promised in a pitch.
Data engineering depth
AI outcomes are decided by pipelines, and we build the ingestion, quality, and lineage layers first rather than bolting a model onto messy data.
Cost-aware architecture
Token budgets, caching, and routing strategies designed in from day one, so inference cost stays a managed variable instead of a monthly surprise.
Human-in-the-loop by default
Review queues and confidence thresholds designed into the workflow, because the fastest way to reliable AI is a well-designed escape hatch.
Full transfer of IP
Prompts, weights, pipelines, and evaluation suites transfer to you at delivery, so your AI capability is an asset you own rather than a subscription you rent.
Fixed-price increments
Each phase is scoped and quoted in writing before work starts, and 60 days of post-launch support plus 12 months of updates come standard on every build.
FAQ's
$ moxace launch --nowReady to turn your data into a product?
Get a free AI feasibility review and a costed build roadmap from our team.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
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