AFDSOLUTIONS

Service

Data Flow Automation — Decision Ready

Deploy with precision. Land data ready to decide on.

Agents are only as good as what they are grounded on. We build the data foundation underneath them — modelled, harmonised, governed and documented — using proven methodologies and Salesforce best practices rather than whatever the last project left behind.

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  • Proven delivery methodology
  • Salesforce best practices
  • Governance from day one
  • Documented, handover-ready

Most AI problems turn out to be data problems

An agent that answers confidently from stale, duplicated or unresolved data is worse than no agent at all — it moves the error downstream, at speed, in front of a customer. The failure is almost never in the model. It is in identity resolution, ownership, freshness and the quiet assumptions nobody wrote down.

We work the layer underneath: the model, the pipelines, the harmonisation rules, the governance, and the documentation that lets someone else pick it up. It is unglamorous work that decides whether everything above it is trustworthy.

Scope

Where we work

  1. Data modelling and architecture

    A model that reflects how your business actually operates, designed for the questions you need answered rather than the shape of the source system that happened to arrive first.

    • Conceptual through physical modelling
    • Data Cloud data model mapping
    • Standard versus custom object decisions
    • Volume, growth and archive strategy

  2. Ingestion and pipelines

    Reliable movement of data into the platform, with the failure modes designed for rather than discovered in production.

    • Batch and streaming ingestion patterns
    • Idempotency and replay handling
    • Schema drift detection
    • Monitoring, alerting and backfill runbooks

  3. Harmonisation and identity resolution

    Turning several partial views of the same customer into one that holds up. This is the step most often under-scoped, and the one agents are most sensitive to.

    • Match and reconciliation rule design
    • Unified profile construction
    • Survivorship and precedence rules
    • Measurable resolution quality

  4. Quality and observability

    Explicit expectations about completeness, freshness and validity, tested continuously so degradation is caught by a check rather than by a customer.

    • Quality rules and thresholds
    • Freshness and volume anomaly checks
    • Lineage from source to consumption
    • Exception routing and ownership

  5. Governance and security

    Clear ownership, access boundaries and retention decisions — recorded, reviewable, and specific enough to answer an audit question.

    • Ownership and stewardship model
    • Field-level classification and access
    • Consent and retention handling
    • Change control for the data layer

  6. Activation and grounding

    Making the foundation useful: segments and calculated insights for marketing, and retrieval that gives agents the right context without over-exposing the estate.

    • Calculated insights and metrics
    • Segment and activation design
    • Retrieval and grounding configuration
    • Sharing boundaries for agent context

Connections

Platforms we engineer on

Data — Azure, GCP, AWS

Warehousing, pipelines and governance on the hyperscaler the data already sits on.

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Agentforce grounding

Retrieval, context and sharing boundaries so agents answer from the right slice of the estate.

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Core Salesforce platform

Object model, automation, sharing and integration on Sales, Service and custom applications.

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Integration layer

APIs, events and middleware patterns that keep systems in step without brittle point-to-point wiring.

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Analytics and reporting

Metric definitions and models that reconcile, so two dashboards stop disagreeing.

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Consumption design

Data Cloud configuration reviewed for what it will actually consume in credits.

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How we deliver

Step 1

Assess

Profile the sources, find the real quality and identity issues, and document what the current estate assumes.

Step 2

Design

Model, harmonisation rules, governance and consumption forecast — reviewed before anything is built.

Step 3

Build

Pipelines, resolution and quality checks delivered incrementally, each increment verifiable on its own.

Step 4

Operate & hand over

Monitoring, runbooks, ownership and documentation transferred to the team that will live with it.

What good looks like

What a sound data foundation gives you

Answers you can trust

Grounding data with known quality, so an agent's confident answer is actually justified.

One version of the customer

Identity resolution with measurable quality instead of an assumed match rate.

Failures caught early

Freshness and quality checks that surface degradation before it reaches a customer conversation.

Predictable consumption

A configuration designed with credit cost in view, not discovered at renewal.

Decisions written down

Model, rules and ownership documented, so the next change does not start with archaeology.

Room to move

A foundation the next use case can build on without a rebuild each time.

Related from AFD

Find out what your data foundation can actually support.

A short assessment tells you where identity, quality and governance would break under an agent — before you build one on top.

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