We put AI to work on real business tasks — answering customers, processing documents, routing requests and drafting the routine work your team repeats every day. Every automation is measured against the hours it saves and built with guardrails, so it is reliable enough to trust in production.
At a glance
Your team copies data between systems, answers the same questions and processes the same documents by hand — every single day.
A promising demo in a chat window is a long way from a reliable tool that's connected to your data and safe to use with customers.
Wrong answers, leaked data and unpredictable costs are real risks when language models are bolted on without engineering discipline.
We start with an audit of where time goes, automate the highest-value tasks first and measure the result against the baseline.
AI that reads from and writes to your CRM, helpdesk, documents and databases — not a separate tool your team has to remember to open.
Grounding in your own content, human review where it matters, evaluation tests, cost limits and careful handling of sensitive data.
The best automations are usually unglamorous: a task done hundreds of times a week, now done in seconds. We help you find those, then build them properly.
Customer support, sales and internal knowledge assistants that answer from your own documents and hand over to a person when they should.
Agents that carry out multi-step tasks across your tools — researching, updating records, drafting replies — with approval steps where needed.
Automations with n8n, Make or custom code that connect your apps and remove manual hand-offs, with AI where judgement is needed.
Smart search, summaries, drafting and classification features added to your existing app or SaaS, with cost and latency under control.
Extracting, classifying and validating data from invoices, forms, contracts and emails — straight into your systems.
Internal tools that give your team an AI copilot for their specific job — proposals, reports, research or operations.
Five stages, with a working prototype early so you can judge the value before committing to a full build.
We review the workflows where time goes, estimate the hours each automation could save and rank them by value and risk.
A working prototype of the top use case, tested on your real data, with an evaluation set that measures how often it gets things right.
Production integration with your tools, guardrails, human-review steps, logging and cost controls.
Staged rollout with your team, training, and monitoring of accuracy, cost and time saved against the original baseline.
Models and prompts improve continuously from real feedback, and the next automation from the audit list begins.
We choose models and tools per task — weighing accuracy, cost, latency and where your data is allowed to go.
the AI and data ecosystem
integrations and product features
APIs, webhooks and real-time assistants
custom models when an LLM isn't the right tool
vector search alongside your business data
queues and caching for AI workloads
the right model for each task
fast, visual workflow automation
Most AI projects start with a short paid prototype, so you see results on your own data before committing further.
Prototypes and first automations
An audit and a two-week prototype at a fixed price, then a fixed quote to take it to production.
Teams automating many workflows
A monthly team that keeps finding, building and measuring new automations with you.
Automations already running
Prompt and model updates, cost checks, monitoring and fixes when your tools change.
Compare AI Automation & Tools pricing with our other services: engagement and pricing.
The best candidates are frequent, rule-heavy tasks with a lot of reading or writing: answering common customer questions, triaging support tickets, extracting data from documents, qualifying leads, drafting replies and reports, and moving information between systems. Our audit ranks your processes by hours saved and risk.
It depends on how many systems it connects to, how much content it needs to understand and how much human review is required. There are two parts: the build, and ongoing model usage. We estimate both during the prototype stage, so you know the running cost before you commit.
We design for it from the start: we use providers and settings that don't train on your data, send models only the information a task needs, mask sensitive fields where possible, and can use self-hosted open-source models when data must stay on your infrastructure.
Accuracy depends on the task and your data, so we measure it instead of promising a number: every automation is tested against an evaluation set of real examples before launch, and we add human review for decisions where mistakes are costly.
We work with leading models such as Claude and GPT, and with open-source models when cost or data residency matters. The model is chosen per task, and the system is built so it can be switched later without a rewrite.
Yes — CRMs, helpdesks, email, spreadsheets, databases and most SaaS tools with an API. We connect directly or through automation platforms like n8n and Make, depending on what's most maintainable for you.
That isn't the goal we design for. We aim to give people their time back: the repetitive part of the job is automated so your team spends more of its day on work that needs a human.
Name one repetitive task your team hates. We'll tell you honestly whether AI can take it over, and what that would take.
