Datapplied connects Datasset, Datagent and Dataccess: your data foundation, the intelligence that works on it, and the ways people and systems consume it.
The technical scope below describes implementation options, not a fixed product feature list. Architecture, source access, hosting, review controls and service requirements are specified for each engagement.
Specify the data asset before building the engine.
Translate the business question into a source strategy, domain model and engineering specification. Discovery reduces uncertainty about what can be obtained, how it should be represented and what makes it fit for use.
Foundation for DatassetMap enterprise systems, documents, web sources, partner data and licensed feeds. Assess authentication, source permissions, extraction routes, usage constraints, coverage and historical availability.
Specify access prerequisites, upstream dependencies and source owners. Separate confirmed access from assumptions that need testing.
Review samples for missing values, duplicates, malformed records, schema variation, language differences and inconsistent identifiers. Examine how fields and formats change between sources and over time.
Measure coverage and distribution where samples support it. Record sampling limits so feasibility conclusions do not imply full-source coverage.
Define entities, canonical identifiers, relationships, cardinality, controlled vocabularies and taxonomies. Agree field types, units, timestamp semantics and the distinction between missing, unknown and not applicable.
Specify source-to-target mappings, identity rules and business definitions. Model examples could include product → variant → offer → seller → location, depending on the domain.
Define parsing, normalisation, entity resolution and cross-source reconciliation requirements. Identify where deterministic rules are sufficient and where model-assisted interpretation needs review.
Describe derived fields, supporting evidence, conflict-resolution rules and the thresholds for accepting, rejecting or escalating a result.
Specify freshness, refresh cadence, historical retention, expected volumes and downstream query patterns. Define the required access methods and the boundaries of the private hosting environment.
Document review ownership, exception handling, recovery expectations and dependencies on customer infrastructure or upstream providers.
Create representative test cases and an agreed evaluation dataset. Define what completeness, consistency, matching quality and successful delivery mean for the use case.
Prioritise the first implementation scope, unresolved questions and technical risks. Turn the findings into a development backlog with explicit acceptance gates.
A source inventory and access assessment, profiling findings, initial domain model and data dictionary, mapping specification, quality criteria, delivery requirements and a prioritised implementation plan.
Discovery can be commissioned independently. The output is an actionable engineering specification; ongoing data operations belong to the development and maintenance scope.
A custom model built for your data, hosted privately.
Build the software that turns fragmented information into a coherent, maintained data asset. Datagent performs defined work against your domain model, with validation and review boundaries designed into the process.
Datagent builds and maintains DatassetImplement access through agreed APIs, database extracts, files, documents or permitted web sources. Select batch, incremental or event-driven ingestion according to source capabilities and freshness requirements.
Design checkpoints, pagination, rate limits, bounded retries and idempotent processing. Preserve source identifiers, collection timestamps and acquisition metadata for traceability.
Parse records and document content into typed fields. Standardise dates, currencies, units, naming and enumerations. Apply schema validation before information enters the canonical dataset.
Where needed, scope OCR or model-assisted extraction with supporting evidence and review. Quarantine invalid records rather than silently coercing ambiguous values.
Implement entities, relationships, taxonomies and business logic as a versioned model. Resolve records across sources through stable identifiers, composite keys and scoped matching rules.
Handle aliases, source precedence, merge/split decisions and relationship changes. Where probabilistic matching is appropriate, evaluate it against labelled examples and route uncertain matches for review.
Link internal records to relevant external attributes and signals. Compute derived fields with documented rules, reference data and dependencies. Keep observed facts distinguishable from inferred or calculated values.
Track provenance and transformation versions. For model-assisted tasks, specify constrained outputs, evaluation cases, permitted tools and human review triggers.
Configure agents around bounded tasks and explicit inputs, outputs and permissions. Separate retrieval, interpretation, validation and publication so an uncertain result does not automatically become trusted data.
Specify credentials and tool access, execution budgets, retry limits and escalation routes. Approval checkpoints, audit records and rollback behaviour are designed where the workflow requires them.
Implement checks for completeness, uniqueness, referential integrity, consistency and freshness. Detect schema changes and unusual shifts in source coverage or field distributions.
Define metrics, alert thresholds and exception queues. Monitor ingestion lag, processing failures and validation outcomes against the agreed acceptance criteria.
Define where the engine and data reside, which environments are needed and who can access them. Agree isolation, network boundaries, secrets management and least-privilege roles for the chosen hosting design.
Specify storage, retention, backup and recovery requirements. Customer infrastructure, residency constraints and operational responsibilities are resolved in the architecture and delivery scope.
Build recurring updates and reconciliation processes. Plan for upstream corrections, deleted records, late-arriving information and schema evolution without losing the history needed by the use case.
Define replay and backfill procedures, versioned transformations and safe releases. Historical snapshots, lineage depth and recovery mechanisms depend on the agreed operating model.
A working data engine and maintained Datasset, documented schemas and transformation rules, source mappings and provenance, validation checks, scoped agent workflows, deployment configuration and operating runbooks.
Refresh frequency, review responsibilities, retention, recovery objectives and maintenance coverage are agreed explicitly. Hosting privately does not replace the need to define access and operational controls.
Engineer the contract between the data and its consumers.
Design Dataccess around application behaviour, agent context and human workflows. APIs, MCP, interfaces and data delivery are complementary access methods, each with its own contracts and operational requirements.
Dataccess delivers the resulting intelligenceDefine resource models, typed request and response schemas, filtering, sorting and pagination. Specify freshness metadata, provenance exposure, error semantics and compatibility expectations.
Scope synchronous queries, bulk endpoints or asynchronous jobs around the workload. Document contracts with an agreed specification format such as OpenAPI where appropriate.
Choose the authentication mechanism around the consumer and deployment environment. Define permissions by resource, operation and data scope, including field-level restrictions where needed.
Specify credential provisioning, rotation and revocation, tenant boundaries and audit requirements. API keys, OAuth or service identity are implementation choices, not assumed integrations.
Design throughput and latency around expected traffic and query patterns. Assess indexing, caching, precomputed views and batch retrieval against freshness and consistency needs.
Agree payload sizes, concurrency limits, rate limits and load-test scenarios. Define backpressure, timeouts and retry guidance; performance targets are established and validated per engagement.
Expose scoped data capabilities as clearly described MCP tools and resources where required. Define input schemas, result structures, entity context, source evidence and freshness information.
Keep credentials and authorisation on the server side. Bound query scope and output size; distinguish retrieved content from instructions and require approval for consequential operations if any are included.
Design focused search, exploration and review experiences around a team's actual decisions. Present source context, quality exceptions and change history where they help users interpret the data.
Scope role-based access, filters, exports and review actions. Confirm accessibility, application identity and integration requirements before choosing the interface implementation.
Specify full snapshots or incremental deliveries, file formats, partitioning and delivery schedules. Map the model into the customer's warehouse, storage environment or downstream pipeline as agreed.
Define watermarks, manifests, schema versions and deletion/correction semantics. Include reconciliation counts or checksums where needed to verify delivery and support replay.
Manage changes to schemas, endpoints and derived fields through explicit compatibility rules. Distinguish additive updates from breaking changes and define deprecation and migration processes.
Use contract tests and representative consumer scenarios. Provide sample payloads, integration documentation and a scoped test environment where required.
Define monitoring, service ownership and escalation for the delivery layer. Record availability expectations, freshness commitments, incident procedures and dependency boundaries.
Agree dashboards or logs, support coverage and recovery procedures. Handover includes documented integration assumptions and the checks needed to confirm successful consumption.
The scoped access interfaces, versioned schemas and contracts, authentication and permission configuration, integration documentation, validation results and delivery runbooks.
The engagement specifies which access methods are built and who operates them. Availability, latency, throughput and support commitments are defined in the delivery scope.
Bring the sources you have, the model you need or the system that must consume the result. We can scope one discipline or connect all three into an implementation.
See the implementation approachTell us where your data is fragmented, difficult to maintain, or hard to use. We’ll start with the question you need to answer.
Talk to us →