AI assistant development
AI assistant development for your business workflows
Softblues develops custom AI assistants that help people find information and complete agreed tasks across business systems. We define the knowledge, permissions, actions and human handover around the workflow, then test the assistant on representative interactions.
Knowledge and system integration · Chat or voice where appropriate · Scoped actions and human handover
- OpenAI Select Partner
- Google Cloud Services partner
- Anthropic Partner Network member
- Member of the Microsoft AI Cloud Partner Program
Do you need a custom assistant or an existing platform?
Claude, ChatGPT or Microsoft 365 Copilot may already cover the knowledge work your team needs. Custom AI assistant development becomes relevant when you need a particular user experience, controlled actions across systems or a workflow that the existing setup cannot support.
We establish that difference before committing to a build. Compare platform options, or explore event-triggered AI agents if the work needs to run without someone starting a conversation.
What can an AI assistant do?
Six capabilities, scoped individually. A build takes the ones the workflow needs rather than all of them.
How does it connect to your systems?
Four steps, and every one of them is a place where a permission or a review requirement can be set.
A request arrives
An authorised user makes a request through the chosen interface.
Permitted context is retrieved
The assistant retrieves permitted context from maintained sources.
A scoped integration acts
A scoped integration performs an allowed action or prepares it for approval.
The work is recorded
The workflow records the agreed activity and passes exceptions to a person.
The implementation may use retrieval-augmented generation, APIs and workflow services. Model selection follows the task and hosting requirements rather than a fixed model-version list.
Which workflows can it support?
Four places an assistant is usually worth scoping. Each one starts as a single workflow rather than a general-purpose deployment.
- Customer service
- Product questions, troubleshooting, ticket creation and escalation with the relevant context.
- Employee knowledge and tasks
- Finding approved information, following company procedures and navigating internal systems.
- Sales support
- Answering product questions, collecting enquiry details and preparing the next step for the sales team.
- Scheduling
- Checking availability and handling agreed booking or rescheduling actions through authorised calendar integrations.
What does delivery include?
We scope the task and data sources, define permissions and review requirements, build the selected integrations and test representative interactions.
A controlled pilot helps assess use, exceptions and review effort. Documentation, handover and any ongoing support are agreed in the scope.
Access, data flows, provider settings, retention and allowed actions are part of the same scope rather than a later conversation.
Evidence
See the work and its current stage
Roof Maker's customer-support case shows a voice assistant connected to a support workflow, including escalation to people. The other two are related assistant work; the label on each card is the case's own.
Live
From Missed Calls to 24/7 Support
Roof Maker's support line went quiet after hours, and routine questions ate up specialist time during the day. Softblues automated the front line of their customer support: an AI voice agent that answers every call, troubleshoots, logs the ticket in HubSpot, and escalates anything urgent to a person. 24/7, in production.
Read the case study
In beta · 10 clients
AI candidate assessment, 2–3 hours to minutes
How Softblues automated SofiaHR's candidate-assessment process: a 2 to 3 hour expert linguistic analysis, scored across 10 psychological dimensions, now runs in minutes at 90%+ of human-analyst accuracy.
Read the case study
In beta
AI-Powered Clinical Research Platform
How Softblues automated a 12 to 18 month manual research process into a nine-agent AI pipeline that turns a plain-English question into a publication-ready analysis in weeks.
Read the case study
Each card carries the stage that project actually reached. None of them is described as a live assistant deployment unless its own stage says so.
Where does the security scope fit?
Access, data flows, model-provider settings, retention and the actions the assistant may take are agreed with your team during scoping, alongside the review points a person keeps.
AI assistant development: at a glance
- What it is
- A custom AI assistant that helps people find information and complete agreed tasks across business systems, with the knowledge, permissions, actions and human handover defined around the workflow.
- Interfaces
- Chat or voice, and selected channels such as a website, Teams, Slack or a business messaging service. Each channel is assessed separately.
- Knowledge
- Retrieval from approved documents and sources, with references where appropriate. Retrieval reduces some errors; it does not guarantee a correct answer.
- Actions
- Scoped integrations perform an allowed action or prepare it for approval. Permissions and review requirements limit what the assistant can do.
- Evaluation
- Answer quality, task completion, escalation, response time and operating cost, against an agreed test set and real usage.
- Before a build
- We establish whether an existing licensed platform already covers the work. A custom assistant is for a particular experience, controlled actions across systems or a workflow the existing setup cannot support.
- Costs
- Knowledge preparation, integrations, channels, evaluation and operational requirements affect development. Model usage, hosting, licences and agreed support affect running costs.
Common questions about AI assistants
What is the difference between a chatbot and an AI assistant?
The terms overlap. In this service, an assistant can use approved knowledge and integrations to help complete a task. The important distinctions are the actions, permissions and review process, rather than the label.
Can the assistant integrate with our existing software?
We assess the available APIs, access permissions and workflow requirements during discovery. The scope identifies the systems to connect and any limitations or preparation work.
How natural does the conversation feel?
It depends on the interface, model, task and response time. We test representative conversations, including misunderstandings and handover, rather than relying on a polished demonstration.
What about privacy and security?
The scope covers access, data flows, provider settings, retention and allowed actions. Hosting an application in your environment does not by itself determine where a model provider processes data. Our security page sets out what we scope with your team.
Can you build an AI voice assistant?
Yes, where voice suits the workflow. We assess telephony or channel integration, response time, escalation and data-handling requirements as part of that scope.
What affects development and running costs?
Knowledge preparation, integrations, channels, evaluation and operational requirements affect development. Model usage, hosting, licences and agreed support affect running costs.
Which task should the assistant help with first?
Tell us who needs help, which systems they use and where the current process breaks down. We will discuss whether an existing platform or a custom assistant is the useful next step.