What This Is
We build AI assistants that live inside your internal tools. These are not public-facing chatbots. They are focused helpers that your team uses daily to retrieve information, draft documents, and automate routine tasks. An internal assistant sits inside your admin panel, CRM, or project management tool and answers questions using your own data, following your own rules.
The difference between this and giving your team ChatGPT logins comes down to context and control. An internal assistant is connected to your databases, documents, and business rules. It knows your product catalogue, your pricing structure, your standard operating procedures. When a sales rep asks “what’s our margin on the enterprise tier for annual billing?”, the assistant queries your actual pricing data and calculates the answer. It does not guess from training data. This work builds on our wider OpenAI API integration, the layer that connects the model to systems it can act on.
We run internal assistants on our own platform. Our operations assistant retrieves client project data, summarises recent activity, and drafts status updates using real records from our project management system. It cut the time spent on weekly client reporting by roughly 60%, because the assistant handles the data gathering and first-draft writing that previously required manual lookups across multiple systems.
When You Need This
Internal assistants make sense when your team repeatedly searches for the same kinds of information across multiple systems, or when they draft similar documents by assembling data from different sources. The pattern is consistent: open three tabs, find the relevant records, copy data into a template, adjust the language.
You might also need this when onboarding is slow because institutional knowledge lives in people’s heads rather than in searchable systems. An assistant trained on your documentation, processes, and historical decisions gives new team members a way to get answers without interrupting senior staff.
How We Work
We start by mapping the knowledge sources, identifying which databases, documents, and APIs the assistant needs access to. This is not about uploading everything into a vector database. We pin down the specific queries the assistant has to answer and build a retrieval path for each one. A pricing question hits the pricing table directly. A process question searches the documentation index. Each retrieval path is tested on its own before the assistant is assembled.
The assistant’s behaviour is defined by a system prompt and a set of available tools. The system prompt sets the tone, scope, and boundaries: what it can answer, what it should refuse, and how it should handle ambiguity. The tools give it access to your data through a database lookup function, a document search endpoint, a calculation service. Every tool has typed inputs and outputs, so the assistant cannot retrieve data it should not see. Where an assistant needs to go further than retrieval and actually do work, we extend it with custom AI tools that wrap your real systems.
We deploy assistants as embedded components in your existing tools. That might be a sidebar in your admin panel, a slash command in your project management system, or a dedicated internal page. The assistant authenticates as the current user and respects existing permission boundaries, so it can only access what that user can access.
What You Get
- An AI assistant embedded in your existing internal tools with single sign-on
- Retrieval tools connected to your databases, documents, and APIs
- Permission-aware access, so the assistant respects your existing role-based access controls
- System prompt with defined scope, boundaries, and refusal behaviours
- Conversation logging for audit, training data review, and usage analysis
- Token usage tracking and per-user cost visibility
- Documentation covering supported queries, data sources, and limitations
Technologies We Use
- OpenAI API: GPT-4 with tool use for structured data retrieval and multi-step reasoning
- Laravel: backend service layer, tool registration, user authentication, and permission checks
- PostgreSQL: conversation history, usage logs, and retrieval source indexing
- Redis: session management, rate limiting, and response caching for frequently asked queries
Related Systems
Internal assistants typically plug into a query management system for structured request handling, or a reporting dashboard for data summarisation. The assistant provides the conversational interface; the system provides the data and workflow.
Put Your Team’s Knowledge to Work
If your team wastes time hunting for information that already exists in your systems, get in touch and we will scope an assistant that retrieves it instantly.
