Your data is trapped in your documents

It means taking the information that is locked inside a document and turning it into rows and columns you can query. An invoice PDF is readable by a person but useless to a system: the supplier, date, line items, VAT and totals cannot be joined, summed or reconciled while they sit in a page image. We use Snowflake’s Document AI and Cortex capabilities to read those documents — including scans and photographs, not just clean digital PDFs — and land the fields you care about in Snowflake as structured data, with the original document retained and linked so every extracted value can be traced back to where it came from.

That is usually the point of the exercise. Processes stall because the information they depend on is captive in a PDF and a person has to retype it before anything can move. Once the data is structured we automate what was waiting on it: matching invoices to purchase orders and delivery notes, flagging exceptions and mismatches for review, routing approvals, reconciling statements, and feeding the results into your finance or operational systems. Confidence thresholds decide what passes automatically and what a human checks, so the exceptions get attention and the routine cases stop consuming anyone’s day.

Snowflake data engineering is the bulk of our work. We design and build warehouses and lakehouses, dimensional and event models, and the ELT pipelines that feed them — using streams, tasks, dynamic tables and Snowpark where they fit. Alongside that we set up the parts teams often postpone: role hierarchies and role-based access control, masking and row access policies, lineage, data quality tests, and warehouse sizing and resource monitors so the bill stays predictable. We work either as the team that builds your platform or alongside your engineers, and we hand over documentation and training so you are not dependent on us afterwards.

Yes. Migrations are a regular part of our work, whether you are coming from an on-premise warehouse, SQL Server or Oracle, a Redshift or Synapse estate, or a sprawl of spreadsheets and operational databases that has outgrown itself. We start by profiling what you actually have and use rather than what the documentation claims, agree a target model, then move in tranches so that something useful lands early and the old system can be retired in stages rather than in one risky cutover. Reconciliation between old and new runs throughout, so you can prove the numbers still match before anything is switched off.

They answer two different questions, and most clients end up using both. Snowflake is where your governed data lives and where most analytics and AI work belongs — with Cortex, inference runs inside Snowflake, next to the data, so nothing has to be exported or copied. AWS Bedrock is what we reach for when you need a specific frontier model, a long context window, or capabilities Cortex does not cover, and we integrate it so that your data stays inside your security perimeter rather than being handed to a third-party service. The choice is made per use case, on the merits, and we will tell you when you do not need the more expensive option.

Through Snowflake Cortex you get Snowflake-hosted models for tasks such as summarisation, classification, extraction, embedding and vector search, running inside your Snowflake account. Through AWS Bedrock you get leading foundation models from providers including Anthropic (Claude), Google (Gemini), Meta (Llama), and others. This gives you flexibility to choose the right model for each use case without managing multiple vendor relationships, and to switch models or use several for different tasks as they improve.

We provide end-to-end delivery: assessment and target design, configuration on your infrastructure or your Snowflake account, pipeline and model build, integration with your existing systems, and the AI workflows built on top. We also provide training and ongoing support so your team can operate and extend the platform independently. Engagements are scoped in writing before they start, and we would rather size the first one small and prove the approach than sell you a programme.

Far less than most people expect. Snowflake is a managed service, so there is no cluster for you to run, and its compute scales up and down with demand. On the AI side you do not need specialised hardware either — unlike hosting foundation models yourself, which requires serious GPU investment, our approach leverages Snowflake and AWS for the heavy computation while your data processing stays governed and local. Standard infrastructure is enough for anything we deploy on your side. We will assess your current setup and optimise the deployment for both cost and complexity.

The same principle runs through both practices: your data stays inside your security perimeter, and access is governed rather than assumed. In Snowflake that means role-based access control, masking and row access policies, and audit trails over who read what. With Cortex, inference happens inside your account and your data is not used to train the underlying models. Where we use AWS Bedrock, AWS applies the same commitment — your data is not used to train foundation models — and interactions occur over encrypted connections with access controls, data sanitisation and audit trails in place. You should not have to rewrite your security policy to adopt any of this.