Engineering applications, integrations, automation, data and AI around the systems and workflows that support go-to-market and revenue operations.
Engineering Across the GTM Environment
Account & Opportunity Intelligence
Bring the right account, opportunity and external context into the work that needs it.
Proposal & RFP Intelligence
Complex requirements and company knowledge into structured response workflows.
GTM Workflow Automation
Connect systems and automate the actions that move GTM work forward.
Customer Service & Revenue Operations
Connect customer context with the systems and actions needed to respond.
A working example
A reference scenario showing how account data, CRM context, external information and AI can work together to support research, qualification and the next action.
The Architecture Is Only Part of the Outcome
A workflow like the one above can be engineered in different ways depending on the systems, data, volume, permissions and level of autonomy involved.
The engineering questions often sit between the visible steps: what context is available, how information is retrieved, which actions a model can take, where human approval belongs, what is written back to the system of record, and what the architecture costs to operate at scale.
01 — Context - Is the model receiving the information required for the task? Missing context can affect the usefulness of the output.
02 — Retrieval - Is retrieved information current, relevant and authorised? The answer is only as dependable as the information made available to it.
03 — Model - Does the workload require generation, reasoning, tool use — or no model at all? More capability can also mean more complexity and cost.
04 — Action - What can execute automatically and what requires approval? Autonomy should match the consequence of the action.
05 — Write-back - What returns to CRM, in what structure, and with what auditability? An AI action becomes operational only when the surrounding systems know what happened.
06 — Cost - What happens to model, API and infrastructure usage as volume increases? An architecture that works technically still has to make sense at operating scale.
Built Around the Technology Involved
We can work with existing business systems, APIs, data and appropriate AI model ecosystems — selecting the engineering approach around the requirement rather than designing the requirement around a preferred technology.
CRM & GTM Systems
Salesforce · HubSpot · Microsoft Dynamics 365 · other appropriate platforms
AI & Model Ecosystems
OpenAI · Anthropic Claude · Google Gemini · appropriate open or specialised models
Engineering
APIs · Webhooks · Retrieval / RAG · Structured Data · Document Processing · Workflow Orchestration · Authentication · Permissions · Observability
The technologies shown are examples. The architecture and technology choices depend on the systems, workload and requirements involved.