We build chat and voice assistants that answer from your own knowledge, with citations, and can take approved actions in your systems. Safety checks, testing and monitoring keep quality steady after launch.
What's included
6 capabilities, delivered by one squad. Use what you need now and add more as you grow.
Assistants for customer support, IT helpdesk and HR policy questions that answer from your own content, with quality you can measure.
Your documents are indexed and kept current, and each answer cites the passage it came from.
Voice workflows for contact centres, with intent routing, call summaries and compliant transcripts.
Assistants that raise tickets, update the CRM, check order status, book appointments and handle approvals.
Test suites, quality scoring and regression checks, plus monitoring once you're live.
Personal data is masked, policy filters block off-limits answers and access control decides who sees what.
Our approach
The usual way
Chatbots built on prompts alone, with no sources, so made-up answers become normal.
How we do it
We index your content, tune how answers are found and add citations, so every answer can be checked against its source.
The usual way
Assistants that can talk but can't do anything in your real workflows.
How we do it
Tool calling connects the assistant to your CRM, ERP and support systems, with controlled permissions.
The usual way
No safety checks or tests, so personal data leaks and quality slips unnoticed.
How we do it
Policy filters, personal data controls, test suites and production monitoring are part of every build.
Architecture
Each layer has a clear job, so the system is easier to secure, test and extend.
Document pipelines split your content into passages and tag them, ready to be searched.
Search tuning, ranking, citations and freshness checks keep answers accurate and current.
The assistant calls your tools and workflows only through permitted actions, with approvals where needed.
Quality tests, response-time limits and regression alerts once the assistant is in production.
How we deliver
4 phases, each ending with an output you can review.
We define what users will ask, which workflows the assistant covers and where its limits are, and agree how success will be measured.
Output: Feasibility blueprint
We build knowledge pipelines, tune search, add citations and set up a first set of quality tests.
Output: Grounded assistant baseline
We connect your systems and add permissions, approvals and activity records, so actions are safe.
Output: Assistant connected to your workflows
Monitoring and regression alerts guide ongoing improvements, and we add new channels as you grow.
Output: Measurable impact on conversations
Your team
Specialists join your squad for this work, alongside a delivery lead who keeps you updated.
Designs how the assistant behaves, how conversations flow, when to hand over to a person and what good quality looks like.
Builds the RAG pipelines: indexing, search tuning, keeping content current and reliable citations.
Handles safeguards, access control, personal data masking and policy rules.
Runs quality tests, monitoring, response-time limits and regression alerts, so the assistant stays stable.
Trust and control
Golden test suites and safety checks run before any change ships.
Role-based access, personal data masking and protected data paths keep sensitive information safe.
Decision logs, tool-call tracing and live monitoring show what the assistant did and why.
A person reviews and approves actions where the risk calls for it.
You keep full ownership of the code, configuration and documentation we create, with no vendor lock-in.
We pick what fits your product and team, not the other way round.
Results
We built a generative AI support assistant that searches product documentation, troubleshooting guides, known issues and internal notes. Support agents answer customers faster, without switching between knowledge sources.
We built a voice AI assistant for a growing service business. It answers customer calls, works out what callers need, books appointments, updates records and transfers complex calls to the right team member.
We built an AI customer service agent for a growing service business. It understands enquiries, answers common questions, collects the right details, books appointments and updates the CRM, and it brings in a team member when needed.
For a large health system, we built a claims platform that matches EHR evidence to payer rules, automates routine authorisations and logs every decision. It is built to HIPAA requirements. Claims recovery rose 42%, and staff saved 15k hours.
A RAG chatbot looks up the answer in your own documents before replying, instead of relying on what the language model remembers. We index your content, tune the search and make every answer cite its source. That cuts made-up answers and lets your team check any reply against the passage it came from.
Assistants can raise tickets, update CRM records, check order status, book appointments and handle approvals. Each action uses a permitted tool with limited access, higher-risk actions wait for a person to approve them, and every tool call is recorded, so you can see what the assistant did and why.
Personal data is masked before it reaches the model or the logs, and role-based access decides which content each user can see. Policy filters block answers that break your rules, and safety tests run before every release, so a new change doesn't quietly weaken these protections.
We build voice agents that answer calls, route callers by what they need, write post-call summaries and keep compliant transcripts. Voice can be added as another channel on the same knowledge base as your chat assistant. One of our case studies is a voice assistant that books appointments and transfers complex calls to staff.
Every assistant has a golden test suite: real questions paired with agreed good answers. Each change is scored against it with rubric scoring and regression checks before release. Once live, we track answer quality, response time and drift, and alert the team when answers start to slip.
Timing depends on how much content the assistant must answer from, how many systems it connects to and which channels you need. We start with a use-case and policy review, then build a grounded prototype with first tests before connecting your systems. A scoped project can kick off in 1–2 weeks.
Chatbot cost depends on scope: the number of channels, connected systems and safety checks. You can agree a fixed-scope quote for a defined assistant, or work with a managed squad on a flat monthly fee, with no hourly bills. After a first call we give you a rough quote range.
More in AI & Automation
An AI strategy and policies that turn into working approvals, access rules and audit records.
Learn more about AI strategy and governanceAgents that plan, act in your systems and check their own work, with approvals and audit logs.
Learn more about Autonomous AI agentsVision systems that detect, track, read and inspect on your own devices, with checks and monitoring.
Learn more about Computer vision and edge AIPrivate LLMs and RAG systems built on your data, hosted in your cloud and owned by you.
Learn more about Generative AI and LLM developmentWorkflow, document and integration automation with exception handling, approvals and monitoring.
Learn more about Intelligent process automationForecasting, anomaly detection and scenario planning built on reliable data and monitored once live.
Learn more about Predictive analytics and data scienceTell us what you need. We'll suggest the right team and a rough quote range, and an NDA is available before you share anything sensitive.