Core Practice

AI Systems That Span Your Stack

AI Systems Engineering is how we connect AI reasoning to your existing technology, not a greenfield build, but an intelligent layer wired into your CRM, ERP, and databases, with voice and virtual assistant interfaces where they create the most value.

We design multi-component AI systems (model selection, integration layers, data pipelines, and evaluation harnesses) that span your stack and work with the tools your teams already use. Deployed in weeks, not quarters.

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No rip-and-replace

AI layer over your existing systems

CRM · ERP · DB

third-party integrations we wire daily

Voice & virtual

assistant interfaces included

How We Build

An intelligent layer, not a replacement

Most organisations don't need to replace their CRM, ERP, or database; they need an AI layer that makes those systems smarter. We design the integration architecture, select the right models for each task, and wire AI reasoning into your existing tools so users get the output directly inside the apps they already work in.

Voice and virtual assistant interfaces go on top where they add value, not as a default, but as a deliberate choice matched to your specific workflows and users.

Discuss your system

Integration-first

CRM, ERP & database connectivity

We start with your existing systems (Salesforce, SAP, Snowflake, Postgres) and build AI reasoning on top rather than beside them.

Model-to-task fit

Right model for each component

Model selection, evaluation harnesses, and latency budgeting matched to your specific data, volume, and user expectations.

Interfaces that fit

Voice, virtual assistant, or in-product

We build the interface that makes sense for your users: embedded copilot, conversational assistant, or voice AI, not one size fits all.

Systems we build

Six types of intelligent system, each designed to integrate with specific tools and deliver value through the interfaces your teams already use.

CRM-Connected AI Systems

AI layers wired into Salesforce, HubSpot, or Microsoft Dynamics: qualifying leads, drafting follow-ups, surfacing next-best actions, and enriching records automatically.

ERP & Finance Integration

Intelligent systems connected to SAP, Oracle, or NetSuite: automating procurement approvals, invoice reconciliation, and financial reporting without replacing your existing stack.

Database & Data Intelligence

AI reasoning over your Postgres, Snowflake, or Databricks data: enabling natural language queries, automated reporting, and semantic search across structured and unstructured sources.

Virtual Assistants

AI assistants embedded in Teams, Slack, or your web app: answering questions, triggering workflows, and connecting users to backend systems through a conversational interface.

Voice AI Systems

Real-time voice AI for call centres, field operations, and customer service: transcription, intent classification, and live action triggering across your existing telephony stack.

Copilots & Decision Tools

In-product copilots, decision-support dashboards, and intelligent search: AI features built directly into your product that help users move faster and make better decisions.

What You Get

Working systems, not proofs of concept

AI layer integrated with your existing CRM, ERP, or database, no rip-and-replace

Virtual or voice assistant interface configured for your team or customer use case

Model selection and evaluation framework matched to your data and latency requirements

Safety guardrails: output filtering, hallucination mitigation, and access controls

Streaming UIs and low-latency API layers that make AI feel fast and responsive

Evaluation harness measuring real-world quality before and after every change

Common use cases

AI systems features we build across B2B SaaS, enterprise platforms, and internal tools.

Intelligent Search & Discovery

Semantic retrieval and re-ranking over your product content, knowledge base, or data catalogue, beyond keyword search.

Document Understanding

Extract structured data from PDFs, emails, and forms and pipe it directly into CRM, ERP, or workflow systems.

Natural Language Reporting

Let teams query your Snowflake or data warehouse in plain English and receive narrative summaries or structured outputs.

AI-Powered Onboarding

Guide users through your product with an adaptive assistant that answers questions and triggers next steps contextually.

Personalisation & Recommendations

Rank and recommend content, products, or actions from your existing data, without building a custom ML pipeline.

Code & Developer Intelligence

Autocompletion, code review, documentation generation, and debugging assistance embedded into developer tools.

CRM · ERP · Data · Voice · AI Ecosystem

SalesforceHubSpotMicrosoft DynamicsSAPOracleNetSuiteSnowflakeDatabricksPostgreSQLTwilioDialogflowMicrosoft TeamsSlackOpenAIClaudeAWS BedrockPineconeWeaviate

Which system should we connect first?

Tell us which tools your team runs on. We'll scope the integration, build the AI layer, and have a working system in production within weeks.

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Frequently asked questions

These are three distinct form-factors, not interchangeable marketing terms. Automation handles repeatable, rule-based tasks: fixed scripts with deterministic outcomes. Assistants help users retrieve, summarise, and navigate information through a conversational interface, best for knowledge work. Agents make decisions, select tools, and take multi-step actions autonomously across systems. AI Systems Engineering spans all three: we wire the right form-factor into your existing CRM, ERP, or database based on the actual workflow, not vendor preference.

No. We build an AI layer that connects to your existing tools (Salesforce, SAP, Oracle, HubSpot, NetSuite, Snowflake) without replacing them. The integration approach is API-first and least-privilege: the AI system accesses only what it needs, and your existing workflows keep running exactly as before alongside the new AI capabilities.

All three. We build virtual assistants embedded in Teams, Slack, or your web app; voice AI for call centres and field operations; and in-product copilots for your SaaS users. The interface is determined by where your team or customers actually work, not what's easiest to build.

Every system we ship includes output filtering, factual grounding (retrieval over your actual data rather than model memory), and an evaluation harness that measures accuracy before and after every change. For high-stakes decisions, we implement human review tiers so the AI surfaces a recommendation and a human confirms before action is taken.

For integrations into standard tools (Salesforce, SAP, Slack), one to six weeks to a production-ready system. More complex multi-system integrations with custom data pipelines typically run eight to twelve weeks. We work in one-week or two-week sprints depending on the project, with a working demo after the first sprint, so you see progress immediately, not at the end.