AI Automation and Assistants
Hours removed from real work, with a person on every irreversible step.
01
In short
What Kramiva means by AI automation and assistants
Kramiva builds practical AI automation: assistants that answer from your own documents with citations, automated triage and drafting for repetitive requests, lead and reporting automation, and connections between the business tools you already use. Anything irreversible — sending, paying, deleting, publishing — requires a person to approve it.
- Who it is for
- Operations, support, sales, and back-office teams where the volume of repetitive work is real and the cost of a silent error is high.
- Discipline
- AI and Automation
02
Scope
What we build, and what is included.
What Kramiva can build
- AI assistants grounded in your own documents and policies
- Knowledge and document search with source citations
- Request triage, classification, and draft responses
- Lead capture, qualification, and routing automation
- Scheduled reporting built from data across several systems
- Integrations between CRM, email, messaging, and storage tools
What a typical engagement includes
- Process audit: where the hours actually go, measured rather than estimated
- Automation design with explicit human checkpoints
- Retrieval over your own documents and systems, with source citation
- Integration with the tools already in use
- Evaluation, guardrails, and production monitoring
03
Delivery
How this one actually runs.
05
Questions
The things you are about to ask.
- What business processes can Kramiva automate?
- Repetitive work with a clear rule or a clear example: triaging incoming requests, extracting data from documents and invoices, drafting routine replies, qualifying and routing leads, reconciling records between systems, and assembling recurring reports. Work that needs genuine judgement is left with people, sometimes with a draft prepared for them.
- How are sensitive actions approved?
- Structurally, not hopefully. Irreversible or externally visible actions — sending an email, issuing a payment, deleting a record, publishing content — require human approval by design. The system drafts and proposes; a person commits. Every decision is logged with what was proposed, on what basis, and who approved it.
- Does every automation need a custom AI model?
- No, and most do not. Many processes are better served by ordinary rules, a scheduled job, or an integration between two existing tools — a rules engine that is right every time beats a model that is right most of the time. Where a language model genuinely helps, we use a hosted one rather than training our own.
- Can the system connect to our existing business tools?
- Usually. Email, CRM, messaging, spreadsheets, storage, accounting, and helpdesk tools generally have integration paths, and feasibility for your specific combination is confirmed during the audit rather than promised in advance.
- Does our data go into a model's training?
- Not under the configurations we deploy. We use provider terms with training disabled, document exactly which provider processes what, and can scope self-hosted models where data residency requires it. Which data leaves your systems, and where it goes, is written down before anything is built.
Related service
Explore AI features inside your product
Design and engineering of AI features inside your existing product — from feasibility and model choice to the interface, evaluation, and cost per user.
Talk to us about AI automation and assistants.
A short brief gets you a real reply from a founder within one business day — an honest read on fit, scope, and budget.