An enterprise AI assistant project typically runs in four phases: discovery, data preparation, pilot and production rollout. A narrowly scoped pilot can often deliver measurable results within a few weeks, while full production takes longer depending on integration and security requirements. Cost splits into one-off items such as development and integration, and recurring items such as model usage, hosting and maintenance.
What phases does an enterprise AI assistant project go through?
Whether it serves employees or customers, an assistant that answers questions from company documents usually goes through these phases. The ranges below are typical for a focused scope; the number of integrations and the state of your data can shift them.
- Discovery (typically 1-2 weeks): Define the use case, target users, data sources, access model and data protection requirements. Agree on success criteria and collect real questions for evaluation.
- Data preparation (typically 2-4 weeks): Gather documents, check they are current, remove or mask unnecessary personal data, then chunk and index the content. In most projects this is where most of the effort goes.
- Pilot (typically 3-6 weeks): Launch a working version with a limited user group. Measure answer accuracy, citation quality and “I don’t know” behavior against the evaluation set, and iterate on feedback.
- Production rollout (typically 4-8 weeks): Integrate with identity and permission systems, add monitoring and logging, run security tests, train users and roll out in stages.
Put a decision gate between pilot and production: if the pilot criteria aren’t met, revisit scope or approach before scaling.
What are the cost components?
Separating one-off from recurring costs in your budget prevents surprise invoices later.
| Cost component | Type | What drives it |
|---|---|---|
| Discovery and solution design | One-off | Number of use cases and stakeholders |
| Data preparation and indexing | One-off (updates recurring) | Document volume, format variety, data quality |
| Application development | One-off | Interface (web, Teams, Slack, website), feature scope |
| Integrations | One-off | Number of systems (document management, CRM, ERP, identity) |
| Security and compliance work | One-off + periodic | Permission complexity, regulatory requirements, testing |
| LLM API usage | Recurring | Query volume, context length, chosen model |
| Vector database and hosting | Recurring | Data volume, cloud vs. on-premises, availability targets |
| Monitoring, maintenance and improvement | Recurring | Support level, how often content changes |
With self-hosted models, API costs are replaced by hardware and operations costs. Which option is more economical depends on usage volume and data sensitivity.
What does the company need to prepare internally?
Project speed often depends more on your organization’s readiness than on the vendor. Before kickoff, make sure you have:
- A business owner who makes decisions, sets priorities and owns the outcome.
- Data sources: which documents are in scope, where they live and which version is authoritative.
- Access rules: who may see what. The assistant must never reveal information a user can’t see in the source system.
- Subject matter experts to supply real questions and review answers.
- Legal and information security to assess GDPR/KVKK, cross-border data transfer and cloud usage.
- IT support for system access and a test environment for integrations.
How do you measure success?
Define success criteria before the pilot, covering both technical and business dimensions:
- Answer accuracy: share of correct and complete answers, as judged by subject matter experts.
- Faithfulness: answers consistent with the cited sources.
- “I don’t know” behavior: no invented answers when the sources don’t contain the information.
- Usage and adoption: active users, repeat usage, user feedback.
- Business impact: fewer support tickets, faster access to information, less time spent on repetitive questions.
- Operational metrics: response time, availability and cost per query.
Build or buy: off-the-shelf SaaS or custom solution?
| Criterion | Off-the-shelf SaaS assistant | Custom solution |
|---|---|---|
| Time to start | Fast | Requires discovery and development |
| Customization | Limited to what the product offers | Fully tailored to your needs |
| Integration | Limited to built-in connectors | Deep integration with enterprise systems |
| Data residency and compliance | Depends on the provider’s infrastructure | Choose cloud region or self-host |
| Cost structure | Per-user or usage-based subscription | One-off development + operating costs |
| Vendor lock-in | High | Can be kept low through architecture |
For standard needs, starting with an off-the-shelf product can make sense. With sensitive data, complex permissions or many systems to integrate, a custom solution usually pays off over the long term. Hybrid architectures that combine the two are also possible.
Why do these projects fail?
- Vague scope: an “assistant that knows everything” turns into a system that does nothing well.
- Underestimating data quality: outdated, contradictory or scattered documents produce wrong answers.
- Permissions as an afterthought: exposing sensitive information to the wrong people can halt the project.
- No measurement: without an evaluation set, you can’t tell whether changes help or hurt.
- No business owner: decisions stall and users never take ownership.
- Unplanned recurring costs: spend grows unexpectedly as usage increases.
- Neglected maintenance after go-live: quality drops as content changes.
How does working with BrotherhoodIO work?
At BrotherhoodIO, we start AI assistant projects with a AI assessment: a 30-minute conversation to review your use case, data sources and constraints, and to talk honestly about whether an off-the-shelf product or a custom solution fits you better. From there we work pilot-first, agreeing success criteria with you upfront. We stay vendor-neutral, comparing cloud and self-hosted model options, and design privacy-first, with access control and data security at the center of the architecture and KVKK and GDPR requirements addressed from day one.
If you’re scoping an AI assistant for your organization, contact us or call +90 532 380 40 41 to map the timeline and cost components to your own situation.
Frequently asked questions
How long does an enterprise AI assistant project take?
It depends on scope, but discovery, data preparation, pilot and production rollout typically span from several weeks to a few months. A narrowly scoped pilot can often be evaluated within a few weeks.
What are the cost components of an AI assistant?
One-off costs include development, integration and data preparation. Recurring costs include LLM API usage, vector database and hosting, and monitoring, maintenance and improvement.
Should we buy an off-the-shelf SaaS assistant or build a custom one?
Off-the-shelf SaaS is a fast start for standard needs with few integrations. If you have sensitive data, complex permissions, many enterprise systems to connect or need self-hosting, a custom solution is usually the better fit.
What does our company need to prepare before the project starts?
A business owner, the in-scope documents and data sources, access rules, real example questions from subject matter experts, and involvement from legal and information security for the data protection review.