Build vs. buy
Build it yourself and “save”
The implementation project is the part you budget for. The engineers, platforms, tokens and maintenance are the part you keep paying for.
Trusted by leading Finnish and international brands
Continuous analytics is an IT project
What a do-it-yourself conversation analytics project really costs you.
In-house build
Your running tab
- Implementation projecthard to estimate
- AI engineers aligning with the businessongoing
- Generic BI and reporting toolsper seat
- Data infrastructure, automation and monitoringongoing
- Testing, versioning and migrating models and promptsevery change
- AI usage on large historical datagrows fast
- Months before any value6–12
Aiwo
Your whole bill
- Implementation feeknown upfront
- Continuous servicesimple pricing
Keeping the analysis up to date and data cleaning, fixing and integrity are included. So is an analytics expert who knows your data, on an agreed SLA.
First results within a month. At least six-figure saving opportunity from day one.
Line by line: what you actually pay for
Questions to consider before you decide to build it yourself.
In-house analytics
Aiwo
Time to business value
In-house analytics
- 6–12 months
Aiwo
- Within 1 month
Domain knowledge
In-house analytics
- Requires processes and tools between the business and the analytics team just to identify what is important.
Aiwo
- 1–2 workshops with your business to get first results that make an impact. We promise a six-figure saving opportunity from day one.
Producing the analysis
In-house analytics
- Requires in-depth technical and qualitative understanding of analytics for conversational data
- AI engineers need to agree with the business on what is necessary to analyse
- AI engineers need to build an extensive set of semantic models and analyses to extract enough information for your business questions
- Quality assurance is often considered as technical validation and business is expected to validate the results
- Often ends in an unmanageable set of prompts
Aiwo
- A team with years of experience analysing data similar to your company's
- Built-in models that extract details from conversational data
- Tailor-made tooling that produces results the business benefits from; 1–2 workshops are often enough to create the whole initial analysis
- We are responsible for the quality of the analytics
Keeping the analysis up to date
In-house analytics
- History and real-time analytics are not the same: history must be managed to keep things traceable over time
- The implementation must optimise costs, as AI usage gets expensive very fast with large historical data
- Updated models and prompts need a testing process and people to verify quality
- Model versioning and migration are needed to keep the analysis coherent over time
Aiwo
- Included in the service
Sharing insights in the organisation
In-house analytics
- Stuck with generic BI and reporting tools to visualise the data
- Ordering reports and analyses from a team with a backlog
- Using a chat-based UI that doesn't really let you track changes over time
Aiwo
- Built for large enterprises with many stakeholders, from the C-suite to product development and customer service teams
- Built-in AI chat answers any stakeholder's question, with statistically valid answers
- Reporting created automatically using AI chat
- Integrations with collaboration tools
“I have a question” or “I need a change”
In-house analytics
- Response time and quality depend on how busy your AI and analytics team is
- Changes in personnel may cause unexpected delays or hardship
Aiwo
- An Aiwo analytics expert who knows your data and business requirements is at your disposal, based on an agreed SLA.
Data cleaning, fixing and integrity
In-house analytics
- Requires infrastructure, automation and code
- Needs work to implement
- Should be continuously monitored
Aiwo
- Included in the service
Integrating with other data
In-house analytics
- Data is available in a single platform if other data sources are integrated
- You will likely need to manage the granularity of the data before integrating
Aiwo
- Data and analytics available via API and MCP to integrate with any solution
- Data is already at the level of granularity that matters to the business
- No limits on the customer and business metadata attached to the analysis, with focus on the attributes that matter most to your business
Cost
In-house analytics
- The implementation is an IT project that is hard to estimate
- The cost of continuous maintenance is often overlooked or underestimated
- Token costs are hard to calculate and depend on the implementation
Aiwo
- An implementation fee and simple pricing for the continuous service, with no additional surprises.
But wait
“Can’t we just use Copilot or Claude?”
General-purpose AI assistants are excellent at answering questions about what you put in front of them. Turning them into continuous analysis of every customer conversation is a different job, and it brings you straight back to the in-house build.
- Generic AI tool
- Data pipelines
- Analytics/BI platform
- Engineers
= Your in-house project
Delivering effective insights continuously from live data is a full-time job.
Every conversation, not a sample
A chat assistant analyses what you paste or upload. Aiwo connects to your channels and analyses all of it, all the time.
Changes tracked over time
A chat window doesn't track how causes evolve. Aiwo keeps history coherent and gives statistically valid answers.
From insight to owner
Aiwo routes each problem and its cost to the team that can fix it, then tracks whether the fix worked.