Brief
You describe the users, the job to be done, current tools and any hard date or constraint. A page is enough.
Services
AI features that sit inside real software, with a person still in charge
Hirvex Technologies implements AI where it removes a measurable step: drafting, classifying, retrieving, routing or summarising. We use proven model APIs and conventional engineering around them—permissions, logs, evaluation and a fallback when the model is wrong. We do not sell a fictional in-house foundation model.
Who this is for. Teams that want AI inside an existing product or operation, not a demo chatbot on a landing page.
Problems we typically meet
These are the patterns that show up before a useful first release—not abstract “digital transformation” language.
- A chatbot was launched with no source of truth and now invents policy.
- Staff paste confidential documents into public tools.
- Automation was promised without an evaluation set or a human override.
- The business wants “AI” without knowing which job should change.
How we approach the work
We name the job, the data the model may see, and how a wrong answer is caught. Retrieval is preferred over hoping the model memorised your company. Sensitive data stays in systems you control. We instrument quality so you can tell if the feature is helping.
What a serious first release can include
Scope is confirmed in discovery. This is the shape of work we are equipped to deliver—not a menu you must buy in full.
- Assistants grounded in your approved content
- Document intake, classification and extraction with review queues
- Workflow automation that calls existing APIs
- Recommendations inside catalogs or knowledge bases
- Role-aware access so models do not leak across tenants
- Evaluation sets and logging for later improvement
- Integration into Laravel, Node.js, Python or .NET products
What we need from you
A useful brief is better than a long one. If you can answer most of the following, we can propose a first slice instead of a theatre estimate.
- Who will use this, and what must they finish in a session?
- Which systems must stay (accounting, payments, identity, a shop)?
- What is already live that we should not break?
- Any hosting region, language or compliance rule that is non-negotiable?
- The date that actually matters—and why.
How an engagement runs
Four steps. No mystery bench.
Scope
We reply with clarifying questions and a first-release outline: modules, roles, integrations and what “done” means.
Build
Named engineers work in visible increments, with staging you can click and a weekly written status.
Run
Launch, handover and a support path. The system is yours to operate—not a black box we disappear from.
Questions we are asked
Do you train your own large language models?
We do not claim a proprietary general LLM. We apply existing model providers and, where justified, smaller specialised models or classical machine learning. Training data and licences are discussed before any fine-tuning.
Can AI run against private company data?
Yes, with an architecture that keeps data in your tenancy and limits what is sent to a model provider. We will not wire production secrets into a consumer chatbot.
Will this replace staff?
Our implementations are designed to shorten tasks, not to silently make irreversible decisions. High-impact actions stay behind confirmation or a queue.
Start a conversation
If this is the work in front of you
Send the users, the job to be done and any hard constraint. We will answer as engineers—not with a canned brochure.