Good questions.
Clear answers.

We build and operate an intelligence capability around your business, giving your people access to trusted data, business know-how and powerful AI that helps them resolve complex problems.

We connect your data, model it around how your business operates, and curate the definitions and knowledge needed to interpret it. We put that intelligence to work through Dash, reporting platforms and other connected tools.

We stay involved to keep the data current, the knowledge accurate and the capability useful.

Organisations where important questions still take too much time and effort to answer.

You may have established reporting but struggle to investigate what sits behind a result. Your people may spend hours assembling information, reconciling figures and explaining exceptions. Or teams may be using AI independently, with different data, assumptions and little visibility of each other’s work.

We help you establish a shared intelligence capability that supports deeper analysis and more consistent decisions across the business.

Our experience spans retail, ecommerce, logistics, warehousing and distribution, alongside work in other sectors.

We are particularly well suited to businesses with operational complexity: multiple data sources, decisions that depend on detailed business knowledge, and performance questions that standard reporting struggles to answer.

Each engagement reflects the industry and the organisation. We work with your people to capture your terminology, commercial drivers and operating rules, so the intelligence supports how your business actually runs.

We begin with a core use case: a valuable business question, recurring process or complicated decision that needs better support.

Together, we agree what success looks like and identify the relevant data sources. We connect and reconcile the data, then model it in a way that reflects your terminology, business logic and operating knowledge. Your subject-matter experts help validate the numbers and the context.

We roll out the result through Dash, reports and other appropriate tools. Once the first outcome proves useful, we extend the capability to additional questions, users and workflows.

We structure delivery into focused phases, each scoped to finish within four to six weeks, or sooner.

The first phase is designed to produce a useful outcome with real data. We validate it with your team before moving into wider rollout.

A larger engagement may involve several phases. Each has clear deliverables and agreed dependencies, including access to data and time with your business experts. We keep the work contained so you see progress and can assess value early.

We look for a problem with clear business value, accessible data and someone who owns the outcome.

That might be understanding margin erosion, investigating inventory risks, identifying service failures or removing hours of recurring analysis. We agree how to assess success before delivery begins.

The first use case also establishes models and knowledge that future work can reuse.

A business owner, access to the relevant data sources and time with the people who understand the decisions we are supporting.

Your subject-matter experts help explain business rules, resolve conflicting definitions and validate results. We manage the technical delivery and make their required input clear.

We agree that involvement upfront so the engagement can move quickly without becoming an open-ended demand on your team.

No. Establishing the necessary data quality is part of our work.

We assess the sources needed for the first use case, identify gaps and resolve what is required for a dependable result. Where information remains incomplete or unreliable, we make that visible.

You can start with a contained problem and strengthen the foundation as the scope grows.

Yes. We build on the investments and expertise you already have.

We can work alongside your internal team, take responsibility for a defined area or operate the broader capability for you. That includes working with existing data platforms, models and reporting environments.

We agree responsibilities early so your team knows who owns delivery, ongoing operation and business decisions.

For the data foundation, we use established platforms such as Google BigQuery, Snowflake and Databricks. Integration tools such as Fivetran and Estuary bring data into that foundation, and dbt helps us build tested, documented models.

Our visualisation experts build world-class business reports in Tableau, Power BI, Sigma and other major reporting suites. We design reports around the decisions your people make, with clear visual explanations and the detail they need to investigate further.

We establish, configure and deploy complete reporting environments, as well as extend and improve existing ones. This includes the reports, data connections, user access and distribution arrangements your team needs.

We select the combination that suits your requirements and existing investments, with reporting and Dash drawing on the same trusted data and business definitions.

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It is a maintained understanding of your business, grounded in your data.

It includes your terminology, metric definitions, relationships, assumptions and operating rules. It also captures the knowledge needed to interpret a result correctly.

For example, understanding margin may require agreed treatment of freight, returns and discounts, along with knowledge of planned promotions or clearance activity. The intelligence layer makes that understanding available for reports, analysis and agents.

The semantic layer is part of this foundation: it defines what the data means and how to use it.

Our context platform helps us curate knowledge and connect it to the data it describes.

We combine structured information, such as transactions and inventory records, with relevant documents, procedures and expertise from your people. We establish which knowledge applies, resolve ambiguity and capture it in a form that can be reviewed and reused.

This gives Dash a clearer understanding of how to approach a question, which information matters and how the business expects it to be interpreted.

Dashlytix and your business subject-matter experts maintain it together.

Your people own the business meaning. We help capture it, connect it to the data and manage its review as the business changes.

Designated reviewers approve changes to shared definitions, assumptions and guidance. This keeps outdated rules and unverified interpretations from becoming accepted context for future work.

Approved knowledge carries forward into future analysis.

When your team clarifies a calculation, explains an exception or corrects an assumption, that understanding becomes available for subsequent questions and workflows. Your people spend less time repeating explanations, and more of the business’s expertise remains accessible.

The value compounds through deliberate curation. An unreviewed conversation does not automatically become organisational knowledge, and the improvement does not depend on retraining the underlying AI model.

Dash is an advanced AI agent purpose-built to investigate and resolve complicated business problems.

We have designed it to excel at business analysis: working through questions that require several steps, connecting evidence across data sources, testing explanations and producing findings your people can examine and use.

Dash combines state-of-the-art AI with trusted data and a detailed understanding of your business. That gives it the context to investigate beyond the initial question and distinguish an important finding from a result that has a straightforward operational explanation.

It also brings exploration, knowledge curation, recurring tasks and agent management into one experience, with control over users and data access.

Dash is designed for questions that require more than retrieving a number.

For example:

• Why is margin falling despite sales meeting budget, and which products, channels or cost changes explain it?
• Where do we have excess stock alongside potential shortages, and what transfers deserve attention?
• Which customers or orders become unprofitable once freight, returns and service costs are included?
• What is driving fulfilment failures across suppliers, warehouses and carriers?
• Which changes in customer behaviour explain a performance shift, and does the explanation hold across different segments?

The depth of an investigation depends on the data and business context available. We establish those foundations around the problems that matter to you.

Yes. One of the most valuable applications is making detailed investigation affordable and repeatable.

An analysis that previously required someone to assemble extracts, reconcile figures and work through dozens of possible explanations can be scheduled to run overnight. Your team can start the day with findings and supporting evidence ready to review.

Examples include investigating margin movements across thousands of products, reviewing inventory exposure across locations, or analysing the cumulative effect of delivery failures and returns on customer profitability.

We configure and validate each workflow against its scope and available data. This makes deeper analysis possible at a frequency that would be difficult to sustain manually.

Yes. You can inspect the data, definitions and queries behind an answer, including the period, filters and scope used.

This helps your team test the explanation, investigate further and identify missing context.

Where the available information does not support a dependable conclusion, that limitation should be visible. Important decisions still deserve review, particularly where the evidence is incomplete.

Yes. Dash supports capturing and reviewing business context, retaining useful insights and scheduling recurring analysis.

It also provides access to the management of users, data permissions and the tasks and agents established for your organisation.

We develop agents with you around specific responsibilities. Each has a defined purpose, approved tools and clear limits on what it may do.

Yes, where the agreed integration and controls support it.

We establish trust through analysis and explanation, then extend into recommendations and approved actions. An inventory agent, for example, might identify a risk, investigate the cause and prepare a proposed response.

Any ability to create or change an operational record requires explicit permissions, testing and agreed approval rules.

No. Your intelligence layer can also support the tools your people already use.

We can expose approved capabilities through MCP, the Model Context Protocol, and other integrations. This allows applications such as Claude, ChatGPT and Microsoft Copilot to access governed data and business context, subject to their supported connections and your organisation’s settings.

Dash provides a purpose-built experience for business investigation and management. Other applications can draw on the same maintained foundation.

Yes. We can provide access through Slack, Microsoft Teams, WhatsApp and other agreed communication channels.

That might include asking a question, receiving a scheduled investigation or being alerted to an issue that needs attention.

Each connection follows the relevant identity and access controls. We also account for the audience of shared channels so private information does not become visible to a wider group.

That is a useful way to describe it: one governed body of data and business knowledge, accessible through several interfaces.

Your reports, Dash conversations and connected tools draw on the same approved definitions and operating context. Your team can choose the interface that suits the task without rebuilding its understanding of the business in each application.

The intelligence remains under your organisation’s control, including who can access it and what agents can do with it.

The underlying definitions, approved context and access permissions remain consistent.

Different interfaces may present information differently. Results can also vary legitimately because of the user’s access, the question’s scope or the data refresh time.

We maintain the shared foundation and validate how each connection uses it. The objective is consistent business meaning and control wherever the intelligence is accessed.

Those environments are powerful for individual work. The organisational challenge is maintaining visibility and control as people develop their own instructions, skills, context and assumptions.

Even with enterprise controls, teams can end up using different working definitions and methods unless someone actively governs them. It becomes difficult to know which interpretation informed an answer, whether it is current and whether the same approach is being used elsewhere.

Dashlytix establishes shared, approved business knowledge beneath those interactions. Your organisation can review the definitions and rules, maintain them centrally and control access across connected interfaces.

We also prepare the data for analysis. Connecting an AI application directly to raw or poorly modelled data can leave it repeatedly discovering relationships, interpreting fields and issuing expensive queries. Our models and knowledge layer do much of that preparation in advance, guiding the agent towards the relevant data, calculations and context.

That supports more consistent, accurate responses and more efficient analysis. Your people can still use their preferred AI tools while the business retains greater control over the foundation those tools rely on.

These platforms provide capable AI tools, particularly for organisations already invested in their ecosystems.

Making them dependable for your business still requires ongoing work. Someone must connect and validate the data, maintain definitions and instructions, test answers, manage permissions and keep the setup relevant as the business changes.

Dashlytix takes responsibility for that work with your team. We bring the technical delivery and business curation together, then operate the resulting capability.

There are also architectural choices to consider. When business knowledge and agent behaviour become tightly coupled to platform-specific features, moving to another ecosystem can require substantial rework. We make portability explicit so your definitions, organisational memory and client-specific configuration remain reusable.

Our scope also extends to the full business outcome: reporting, deeper investigation, communication channels and governed workflows across the relevant data sources.

If you have the internal capacity to build and maintain this within Snowflake or Databricks, their tools may meet your needs. Dashlytix provides the expertise and ongoing operating responsibility, with flexibility across platforms. Either platform can form part of the foundation we build.

The work supports both established reporting and the questions that follow.

Your Tableau, Power BI or Sigma reports can continue to provide the views your team relies on. Dash adds the ability to investigate why something changed, examine competing explanations and explore questions that were never built into a dashboard.

Both use the same maintained data models and business knowledge. Improvements to that foundation can benefit reporting and AI together.

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Security is central to how we build and operate our platform. We commission independent security audits on an ongoing basis as part of our platform-wide security programme.

We build on established, top-tier software providers with recognised security certifications and independently audited controls. We also configure and maintain the security of each client environment, including identity, permissions, hosting, logging, retention and monitoring.

Users and agents can access only the information and tools they are authorised to use. Those permissions remain consistent across Dash, reporting and connected AI or communication channels. A different interface does not create a route around the underlying controls.

We enforce access through the data and integration architecture, test the relevant boundaries and review changes as the capability expands. Agent actions have explicit limits and approval requirements appropriate to their purpose.

We test the foundation and the way it is used.

That includes validating data pipelines, reconciling important figures, testing models and reviewing real business questions with your subject-matter experts. We monitor for failures and changes that could affect the reliability of an answer.

We also maintain the definitions and assumptions behind the analysis. Reliable data can still produce a misleading conclusion if the business context is wrong.

The managed service keeps both under review, with support available when your team questions a result or identifies something that needs correcting.

Your data and business knowledge belong to you.

Your data, definitions, business rules and client-specific context are never shared with other clients or used in other client projects. We keep each client’s information separate and confidential.

Only authorised people and the service providers required for your agreed environment may process that information, within the agreed access and data-handling arrangements.

You retain ownership of the semantic layer and organisational knowledge developed with you. We also make ownership and handover arrangements for client-specific code and configuration clear in the engagement.

We design the capability to preserve your choices.

Your knowledge, semantic models and organisational memory remain assets you control. We use modular components so you can change interfaces and select suitable frontier AI models from different providers without starting again with your business context.

That includes making the client-specific agent harness explicit: the instructions, tools, memory and controls that determine how your agents operate.

Technology changes still require testing, and platform migrations can involve work. Our aim is to keep that work manageable and avoid unnecessary dependence on a single vendor.

We support an agreed handover of the assets, access and operating knowledge your team needs.

That can include models, curated context, documentation and client-specific configurations, together with the procedures required to maintain them.

Our ongoing relationship earns its place through reliable operation, expertise and improvement. You retain ownership of the business capability we develop together.

We price each engagement according to the data sources, use cases and scope of work.

The proposal sets out the implementation deliverables and investment, followed by the ongoing managed-service scope and fee. Each delivery phase has an agreed scope so you can assess the investment against the intended outcome.

We also identify any separate platform licences, infrastructure charges or other third-party costs.

First and foremost, it ensures your people have continuous access to trusted, current data and business know-how.

We maintain the data pipelines, run tests and monitoring, and keep the environment operating. We maintain models and work with your experts to keep definitions and business logic up to date.

The service also gives your team support when questions, discrepancies or new requirements arise. We investigate issues and maintain the capability as your business changes.

Support arrangements and the capacity for further improvements are agreed upfront.

The fee reflects the data sources, capabilities and level of support included in the engagement.

Our commercial commitment is to keep it significantly below the cost of hiring equivalent in-house capability. You gain ongoing access to senior data, analytics and AI expertise for a fraction of the cost of assembling and maintaining that team.

We make the scope and costs clear so you can assess the full investment.

Yes. The managed-service fee includes AI token usage up to a reasonable cap agreed for your engagement.

We set that allowance around the expected use of Dash and the tasks and agents in scope. We monitor consumption and discuss any need to increase the allowance as usage grows.

The agreement makes clear what is included and how usage above the cap is handled.

We agree success measures for the first use case and review the results with you.

These might include time saved, faster investigation, fewer reconciliation issues or earlier identification of operational risks. For deeper analysis, we also consider whether your people can now investigate questions that previously took too much time or effort to pursue.

Further rollout follows the evidence of value.

Bring one recurring question or business problem, along with the data sources you believe may help answer it.

We will work with you to define a useful first outcome, assess the available information and agree what it will take to deliver.