Gold Standard Phantoms
150+ employees, 3 month engagement.
25 to 30%
increase in operational efficiency
AI Solutions
The data foundation your AI programme depends on.
AI fails on data far more often than on models. We build the pipelines, warehouse and governance that make your data trustworthy, then apply machine learning on top of it.
HIPAA awareSOC 2 (in progress)ISO 27001 (in progress)5.0 on Clutch
Where organisations get stuck
Our data lives in five systems and none of them agree.
Two teams produce different numbers for the same metric.
Our AI programme is stalled on data quality.
Governance and data residency requirements are blocking progress.
The cost of getting this wrong
AI that cannot be trusted
Models and agents trained on inconsistent data produce output nobody acts on.
Decisions on guesswork
When reporting is contested, leadership defaults to intuition and the value of the data estate is zero.
Wasted analyst capacity
Skilled people spend their time reconciling spreadsheets instead of producing insight.
Compliance exposure
Ungoverned data with no lineage or access control fails audits and creates breach risk.
What we deliver
Data architecture and pipeline engineering
Reliable, monitored pipelines from every source system into a single governed platform. Built to be maintainable by your team, not only by us.
Warehouse and lakehouse implementation
Snowflake, BigQuery or Databricks implemented with sensible modelling and cost control. Structured so analytics and AI can share one source of truth.
Data quality, cleansing and unification
Deduplication, validation, entity resolution and automated quality monitoring. You find out when data breaks before a report does.
Governance, lineage and access control
Documented lineage, role based access, cataloguing and residency controls. This is what turns a data platform into one you can put in front of an auditor.
Analytics and business intelligence
Dashboards and self service reporting tied to agreed metric definitions. One number per metric, defined once.
Applied machine learning and AI foundations
Forecasting, prediction, segmentation and anomaly detection, plus the RAG and vector foundations agents need. Models deployed and monitored, not left in notebooks.
How we deliver
Phase 1
Audit
Source system inventory, data quality assessment and requirements.
- Deliverable:
- Data audit and gap report.
- Timeframe:
- Weeks 1 to 3
Phase 2
Architect
Target architecture, data model, governance design and tooling selection.
- Deliverable:
- Architecture and governance design.
- Timeframe:
- Weeks 3 to 5
Phase 3
Build pipelines
Ingestion, transformation, quality monitoring and warehouse implementation.
- Deliverable:
- Production pipelines and governed warehouse.
- Timeframe:
- Weeks 5 to 14
Phase 4
Model
Analytics layer, dashboards and applied machine learning where justified.
- Deliverable:
- Dashboards and deployed models.
- Timeframe:
- Weeks 12 to 18
Phase 5
Operationalise and govern
Monitoring, lineage documentation, access control and team enablement.
- Deliverable:
- Governed platform, documentation, trained team.
- Timeframe:
- Weeks 18 to 22
What you get
- Production data pipelines with monitoring.
- A governed warehouse or lakehouse.
- Documented data lineage and a catalogue.
- Live dashboards on agreed metric definitions.
- Deployed and monitored models where applicable.
- AI ready data access for agents and applications.
- Team training and full ownership.
Security, governance and compliance
- Role based and row level access control.
- Documented lineage for audit.
- Data residency options by region.
- Encryption in transit and at rest.
- PII handling, masking and minimisation.
- No client data used to train foundation models.
Integrates with your existing stack
Systems we connect to
SalesforceHubSpotSAP and ERP systemsEHR and practice management systemsSnowflakeBigQueryDatabricksPostgreSQLMySQLMongoDBGoogle AnalyticsStripeIoT and telemetry sourcesPower BILookerTableau
Technology we use
Platforms and frameworks
PythonSQLdbtAirflowSnowflakeBigQueryDatabricksPostgreSQLKafkaVector databasesscikit-learnPyTorchPower BILookerAWSAzureGoogle Cloud
Proof
Aggregate results from Appnox engagements. Individual outcomes vary by scope and starting point.
Enterprise operations platform modernization
300+ employees, 6 month engagement.
25 to 30%
increase in operational efficiency
Client name withheld under NDA.
How we engage
AI Readiness Assessment
A fixed scope, fixed price assessment that produces a costed, prioritised plan before you commit build budget.
Production Build
A senior delivery team building and shipping the system to production, with agreed scope, milestones and success criteria.
Dedicated Pod
A monthly engineering pod embedded with your team for continuous delivery, iteration and support.
You own all IP. We never train foundation models on your data.
Frequently asked questions
Messy data is the normal starting point and is not a reason to delay. The audit phase exists to quantify exactly how bad it is and what remediation costs. The only situation where we advise waiting is when the source systems themselves are about to be replaced.
Talk to us
Talk to an AI Solutions Architect
Tell us what you are trying to solve. A senior architect, not a salesperson, will reply within one business day with a view on scope, sequence and realistic timelines.
- Senior team, no junior handoff after the pitch.
- Fixed scope and fixed price wherever the scope allows it.
- You own all IP, source code and documentation.