What kinds of AI assistants and agent systems has Wavesteam worked on?

Wavesteam's publicly documented AI work includes enterprise knowledge Q&A, long-form professional document processing, BMS and IoT data assistants, recruiting automation, and user-facing language-learning products. We do not sell one generic “agent template.” Our role is to connect AI with the client's approved information, devices, and operational systems.

This answer deliberately covers only work supported by public case studies or solution pages. General capabilities offered by a model provider are not presented as Wavesteam project experience, and we do not infer accuracy, commercial impact, or implementation details that a public page does not disclose.

Publicly documented areas

Area	Public evidence	What AI does	Integration focus	Relevant organizations
BMS and IoT assistants	BMS battery management and drone battery swapping	Helps users interpret device status, alarms, and technical material	Device identity, telemetry, alarms, and permissions	Battery, energy-storage, swapping, and equipment businesses
Professional and multilingual documents	language-learning case and financial-document automation	Terminology alignment, content checks, OCR, tables, and structured extraction	Document versions, source traceability, and human review	Education, publishing, finance, and document-intensive teams
Recruiting support	AI recruiting system	Resume parsing, initial screening support, and role/candidate information organization	Recruiting workflow, access control, and human decision-making	Organizations with recurring hiring volume and explicit role criteria
Enterprise knowledge and operations	Enterprise AI capabilities in our solutions catalog	Answers from internal sources and can use controlled tools for orders, inventory, or similar data	RAG, enterprise identity, APIs, and audit trails	Teams with repeated questions, fragmented documentation, or many systems
User-facing AI products	language-learning case	Learning content, guided conversation, and quality checks	Accounts, content operations, and feedback loops	Education, content, and membership-product teams

What these projects have in common—and where they differ

A knowledge assistant is not mainly a chat interface. Its difficult work lies in document cleanup, version control, effective scope, citations, and access rights. A device assistant also reads time-stamped operating data. The model may explain an alarm, but deterministic control stays with device rules and operational software. Document automation instead emphasizes page structure, fields, terminology, and evidence for review; one broad “answer accuracy” number is not a suitable acceptance metric.

Recruiting AI must remain decision support. Resume parsing and information organization can be automated, while rejection policy, discrimination risk, personal-data access, and hiring accountability remain governed by the employer and its people. A consumer-facing application adds account management, content safety, payments, operations tooling, and user feedback. Its scope is much larger than one model API integration.

How to judge whether a case is relevant to your project

Compare the data and business loop, not just the industry label. A manufacturer's service assistant may resemble a document-RAG project. An agricultural-drone product focused on batteries and swapping may be closer to BMS and IoT work. The assessment should identify which published modules are genuinely reusable, which interfaces and policies are unique, and whether the public evidence covers the intended market, scale, and security profile.

For a useful assessment, the client can provide redacted representative material, the current workflow, an interface inventory, and the business measure they most want to improve. Wavesteam then selects the most relevant demonstration and validates a small sample. Without documents or interface information, we can discuss capability direction but cannot responsibly promise schedule, accuracy, or financial return.

Choosing the delivery model

Delivery model	Best fit	What the client receives	Limitation	Recommendation
Standard SaaS	Common workflow with little integration	Rapid access to established features	Less control over data boundaries and differentiation	Prefer SaaS when it already solves the job
Integration with existing systems	Existing ERP, CRM, IoT, or content platform	AI added to the current workspace and source systems	Depends on interface quality and cooperation from system owners	Evaluate first for many enterprise projects
Custom product development	Distinctive workflow, owned product, or private deployment	Dedicated front and back ends, workflows, and deployment assets	Higher investment and ongoing ownership	Choose only when long-term value is clear

Wavesteam's relevant strength is delivering AI as part of a complete software system: user and admin experiences, identities and permissions, integrations, logs, deployment, and iteration. This table describes a capability range; it does not claim that every referenced project contains every module.

Evidence

• BMS battery-management case
• AI recruiting-system case
• Language-learning case
• AI financial-document automation solution

These Wavesteam pages are first-party evidence of the project directions described above. Specific delivery scope should be confirmed through the relevant contract, acceptance evidence, or an authorized demonstration.