Generative AI and Assistants: AI assistants that speak with your company's knowledge.
We securely connect large language models to your organization's documents, product information and systems. We build assistants that improve customer experience (CX) and employee experience (EX), along with smart features embedded in your products.
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Generative AI and AssistantsIllustrative interface, sample data
In short
An enterprise AI assistant is software that answers questions from employees or customers, drafts text and carries out defined actions using your company's documents, product information and business systems as its source. CoreTechs builds these assistants with large language models and a RAG (retrieval-augmented generation) architecture, so every answer is grounded in sources you have approved and cites them. They can run in the public cloud, a private cloud or on-premise, with role-based access and data protection requirements designed in from the start.
Scope
Customer side
- Website and WhatsApp chat assistants
- Call center support assistants
- Personalized product recommendations
Employee side
- Internal knowledge assistants (RAG)
- Contract and document summarization
- Email and report drafting
- Coding assistants for software teams
Agent-based solutions
AI agents that can take actions across multiple systems and work with human approval.
Security and compliance
- On-premise or private cloud deployment options
- Data masking
- Role-based access
- KVKK compliance
Who is it for?
- Companies whose support teams answer the same questions over and over and want to respond 24/7 on their website or WhatsApp
- Organizations where procedures, contracts and technical documents are scattered across folders and staff lose time finding the right answer
- Software and SaaS teams that want to add built-in AI features such as summarization, recommendations or natural-language search
- Contact centers looking for an assistant that suggests the right answer to agents during a call
- Finance, healthcare and public-sector organizations that must use AI without sending data outside their environment
- Teams that spend much of their day drafting proposals, reports and emails
How we work
Feasibility and use case selection
We define which questions the assistant will answer, which sources it will use and how success will be measured. We run a short trial on a sample of your own documents and share an accuracy report.
Preparing knowledge sources
We clean your documents, FAQ content and system data, split them into chunks and load them into a vector database. Who can see which source is defined at this stage.
Building the assistant
We implement model selection, prompt design, source citation and hand-off-to-human rules. We work in two-week sprints and show you a usable version at the end of each one.
Evaluation and security testing
Using a test set built from real questions, we measure answer accuracy, source usage and permission boundaries. We verify that the assistant says "I don't know" instead of making things up and never reaches data the user is not authorized to see.
Integration and launch
We connect the assistant to your website, WhatsApp, intranet or systems such as your CRM and ERP. We start with a controlled user group and expand the scope based on feedback.
Monitoring and improvement
We track unanswered questions, user feedback and usage costs. We keep knowledge sources up to date and regularly refine models and prompts.
How long does it take?
The timeline depends mostly on the state of your knowledge sources, the number of systems to integrate, the number of channels and the deployment model. A single-channel knowledge assistant working from clean documents and an agent that takes actions across several systems do not follow the same schedule. We discuss scope in a free 30-minute intro call, usually start discovery within a week, and confirm the exact timeline in writing in the proposal that follows discovery.
Phases
- Intro callA free 30-minute call to discuss the use case, the state of your data and feasibility.
- FeasibilityA short trial on your own data, followed by an accuracy report and a recommended architecture.
- Discovery and fixed proposalWe clarify sources, integrations and security requirements, then share a fixed scope, timeline and price.
- DevelopmentTwo-week sprints, with a working version we test together at the end of each sprint.
- Launch and handoverControlled rollout, team training, and transfer of source code and intellectual property rights.
What drives the cost?
The cost of an enterprise AI assistant has two parts: a one-time development cost, and model and infrastructure costs that grow with usage. We estimate both separately during the feasibility stage.
| Factor | Effect on cost |
|---|---|
| Deployment model | Cloud-based model services allow a fast start; on-premise or private cloud deployment adds hardware and operating costs but keeps full control of your data. |
| Volume and format of knowledge sources | Clean digital documents are processed quickly; scanned PDFs, table-heavy files or frequently updated sources need extra preparation and an automated refresh pipeline. |
| Number of integrations | An assistant that only answers questions and an agent that creates records in your CRM, ERP or ticketing system differ significantly in development and testing effort. |
| Usage volume and model call costs | Model providers charge by the amount of text processed. Monthly costs rise with users and questions; model choice and caching keep them in check. |
| Number of channels | Website, WhatsApp, intranet and contact center screens each mean a separate interface, session handling and testing. |
| Access control and data protection | Per-user source permissions, masking of personal data and log retention rules widen the architecture and the test scope. |
| Languages and accuracy expectations | Multilingual use and domains that demand high accuracy require larger evaluation sets and more improvement cycles. |
Technology options
Language models
- OpenAI
- Anthropic
- Open-source language models (for on-premise use)
RAG and search
- pgvector
- Elasticsearch
- Embedding models
- Hybrid search (keyword + semantic)
Application layer
- Python
- Node.js
- Next.js
- REST APIs
Channels
- Website chat widget
- WhatsApp Business API
- Intranet
- Contact center screen
Infrastructure and security
- PostgreSQL
- Redis
- Docker
- Kubernetes
- AWS, Azure or Google Cloud
- On-premise servers
Example solution scenario
Internal knowledge assistant for HR and company procedures
Representative scenario, not a real client project.
- Profile
- A mid-sized company with staff across several locations and procedures stored in different folders and formats.
- Need
- Questions about leave, expenses, purchasing and information security kept landing on HR and administration teams. Some documents were outdated, and employees could not tell which version was current.
- Approach
- We identified the current procedures together with their owners and excluded outdated versions from the sources.
- We indexed the documents with a RAG architecture and showed the relevant document and section under every answer.
- We defined access rules by department and role so sensitive documents were visible only to authorized staff.
- We built a hand-off flow that turns questions the assistant cannot answer into requests for the right team.
- We launched at a single location first and refined sources and prompts based on feedback.
- Technologies
- Anthropic
- pgvector
- PostgreSQL
- Python
- Next.js
- Single sign-on (SSO)
- Outcome
- Most recurring procedure questions moved to the assistant, freeing the teams to focus on exceptions. Because unanswered questions were reported, it also became clear which procedures were missing or unclear.
Frequently asked questions
Does our data get sent to the AI model or outside the company?
With a cloud model service, only the text fragments needed to answer a question are sent, under enterprise terms where data is not used for model training. If data must never leave your environment, we deploy open-source models on-premise or in a private cloud. Personal data can be masked before it is sent.
Can an enterprise AI assistant comply with GDPR and KVKK?
Yes, but compliance has to be designed in. During discovery we clarify which personal data is processed, where it is stored, who can access it and how long logs are kept. Deployment options without cross-border data transfer are available, and we work alongside your data controller and legal team on the legal assessment.
What if the assistant gives a wrong or made-up answer?
The risk cannot be eliminated entirely, so the architecture is built around it. The assistant answers only from approved sources, cites them and says so plainly when the information is not there. Before launch we measure accuracy against a test set of real questions, and we add human approval for critical actions.
Which AI models do you use?
Depending on the need, we choose between OpenAI and Anthropic models and open-source language models that can run on-premise. We decide during feasibility by testing on your own data for accuracy, language performance, cost and data requirements. The architecture makes it easy to switch models later.
Can it connect to our existing systems such as CRM, ERP or ticketing?
Yes. We connect directly to systems with APIs, and through a middleware layer or database integration to those without. The assistant can read from these systems or, with defined permissions, create records and check statuses. We recommend a human approval step for any action that changes data.
What does the cost depend on?
The one-time development cost depends on the state of your sources, the number of integrations and channels, and your security requirements. On top of that come model and infrastructure costs that vary with usage. We share a budget range after the intro call and a fixed proposal after discovery.
How long does an AI assistant project take?
It depends on how ready your sources are, the number of integrations and the deployment model. We usually start discovery within a week of the intro call and confirm the exact timeline in writing after feasibility and discovery. Development runs in two-week sprints, each ending with a working version.
Does it need maintenance after launch?
Yes. As documents change, sources need updating, unanswered questions need reviewing, and model versions and costs need monitoring. We can take this on through SLA-backed maintenance and support, or train your team to run it.
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