30-Day Conversation Intelligence Pilot on your own calls
We build a working pilot on your own data in 30 days: one structured output, and an honest accuracy read on your calls.
From the team behind Snaplore, whose clients report up to 60% less time on documentation.
The offer, in full
- Fee
- Fixed scope, agreed before we start
- Duration
- 30 days
- You receive
- 4 deliverables, one of them running
- What we need
- A sample of real conversations, and a technical contact
- Typical start
- 2 to 4 weeks from signature
- Ends with
- A working pilot and an accuracy assessment
NDA before any access. Read-only, least-privilege, and agreed with your technical contact before anything starts.
01Track record
Conversation AI experience behind your pilot
We build and operate two conversation AI products, bringing that production experience to your pilot.
- 100+Senior in-house engineers, six countries
- 5.0Unified rating across 61 client reviews on Clutch
- ISTQBISTQB-certified QA engineers
- ISO 27001Certified security practice, audited by Quay Audit UK
02Overview
Test one output on your own recordings
A conversation intelligence pilot is a short, fixed-scope build that runs transcription and a language model over a slice of your real calls or meetings and produces one agreed structured output, so accuracy gets judged on your own data instead of on a vendor demo. Ours takes 30 days, using Whisper or AssemblyAI for transcription and GPT for processing. Conversation and voice intelligence is the specialty behind it.
03The answer
Evaluate the output before funding production
Standard conversation intelligence tools fit when their outputs meet your needs. An internal prototype can test a custom output. Our pilot adds a defined 30-day scope, a working pipeline and a production estimate.
- Test 01
Does it hold up on your audio?
We evaluate output from recordings with your accents, background noise, product names and people talking at once. Your team checks worked examples, so the accuracy read reflects the recordings you want to use.
- You see
- Output from your own recordings
- Test 02
Is the output worth acting on?
We build the agreed structured output in your format, then evaluate its usefulness with the people who would use it.
- You see
- The agreed output, in your format
- Test 03
What would production actually take?
We identify where the output is reliable and what needs refinement, then cost the integration points and delivery sequence for production.
- You see
- A scoped build and an estimate
Choose the trial that matches your build decision
A product trial evaluates its standard output on your audio. This pilot builds one agreed output in your format, with the pipeline and accuracy findings yours to carry into production.
04Fit
When to start a conversation intelligence pilot
The pilot fits teams with recordings they can use lawfully and one output to validate.
Test a defined conversation output
Good fit- You have recorded calls, support conversations or meetings and want to turn them into usable structured information.
- You want to evaluate a summary, a compliance check or a CRM field against examples your team can review.
- You want product claims evaluated on your accents, vocabulary and workflow.
- You need a 30-day accuracy assessment and costed plan to make the production decision.
Prepare the input or proceed to production
Better first step- You are choosing where AI should contribute across the business. Start with the AI Readiness Assessment.
- You need a usable recording sample and an agreed way to retain it. Data engineering prepares the input.
- You have validated the output and want it integrated into production. AI Integration Services delivers that separately scoped build.
- You need security evidence for a buyer's review. Prepare it with the Security and Compliance Readiness Sprint.
We run the pilot on your own recordings, so the accuracy figure describes your calls. That includes two people talking at once, your product names and a caller on speakerphone in a car. Your build decision then rests on evidence.
Andrii BurdaSenior Engineering Manager05Deliverables
Your pilot outputs on day 30
Review the working pilot alongside the evidence and estimate for your production decision.
- 01
A pilot running on your data
We run a slice of your real calls or meetings through the pipeline end to end.
- Format
- Working software, access for your team
- 02
One structured output, built properly
We produce the single output agreed on day one, in your format and ready for your team to evaluate.
- Format
- The agreed output, in your format
- 03
An honest accuracy read
Where the pattern holds on your audio and where it does not, against real examples you can check yourself.
- Format
- Findings log, with worked examples
- 04
A scoped production build
The plan sets out the integration points, sequence, team shape, timeline and cost for the production build.
- Format
- One-page plan and estimate
06Delivery
How your 30-day pilot runs
Your team helps evaluate the output while we build the pipeline.
- Scope the signal (days 1 to 5)We agree the recordings, one structured output and success criterion. Read-only, least-privilege access is settled before any audio moves. Most engagements start within two to four weeks.You getThe success criterion, agreed in writingFrom youA technical contact, and a sample of real conversations
- Build the pilot (days 6 to 20)We build the transcription and processing pipeline on your data and tune it against real examples for your team to review.You getA working pipeline on your own audioFrom youAccess to the recordings, and someone who knows the domain vocabulary
- Test on real conversations (days 21 to 27)Your team uses the pilot on real calls or meetings and reviews the structured output in practice.You getHands-on access for the people who would use itFrom youA few hours of real use, and what you think of the output
- Readout and decision (days 28 to 30)We review accuracy and limits with you, then present the scoped production plan for your decision.You getThe accuracy read and a costed build planFrom youA working session with whoever makes the call
07Proof
Production experience behind your pilot
Our conversation products and operator-reviewed AI work inform the pilot's evaluation. Our education-platform work includes AI tagging and embeddings.
- Conversation products we build and operateSnaplore turns meetings, calls and screen recordings into searchable knowledge using Whisper, GPT and AssemblyAI. Talkmetry applies GPT and AssemblyAI to call intelligence for HubSpot. Both are owned, revenue-generating products using the audio-to-structured-output pattern behind this pilot.2Conversation-AI products we build, run and sell
- A proof of concept that became the systemIn a human-in-the-loop recognition build that doubled data-entry throughput, an operator corrects or releases each record before it enters the database. The client explained the starting point in an anonymized Clutch review: "We first needed a proof of concept to ensure that everything was working."2xData sets processed per month, from about 5,000 by two operators typing by hand
- Sample handling agreed before transferWe agree where transcription and model processing run and what is retained before any audio moves. The security setup addresses prompt injection, data leakage and model access under our ISO 27001:2022 certified practices.ISO 27001Certified security practice, audited by Quay Audit UK
08Case studies
AI products and automation in production
Snaplore turns conversation data into searchable knowledge. Our internal agent increased timely work-time logging compliance by 30% across our own team.
See all case studies
Built by unicrewAI-powered knowledge managementSnaplore is unicrew's own product, built and operated in-house. It uses AI to transform how organizations document, structure, and share information, making meetings, training, and project discussions instantly accessible and actionable.Up to 60%Less time on documentation
Built by unicrewAI-Powered Automation for Project ManagementAn AI bot that enhances operational efficiency by automating a time-consuming internal administrative task.30%Improvement in timely work-time logging compliance
09Client voices
Clients on AI, automation and data engineering
Our headline result was a controlled experiment showing a roughly 9 percent lift in search success rate. The features built are live in production, and the AI tagging and embeddings work set up our move to vector search. What stands out most is their ability to own work end to end, from user-facing search features to the AI and data layer underneath.
Two people typing data into the database went about 5,000 new data sets per month. With this component, we’re reaching twice the amount of data sets per month. Their knowledge of different technologies is astounding. We can raise any technical issue with them and find someone within their team to work with that specific technology.
We were able to get the work completed in the expected time frame. There were little to no defects which was very nice because it allowed us to release and move on to our next project without having to back peddle. They were very accessible and took the time to understand our needs. They truly felt like part of the team.
The quality of their coding was outstanding to our standards. Overall, their work was key for us. They were dedicated to solving our problems as a customer; their team was collaborative, listened to us, and fully engaged in our space to commit to the project.
Artelogic’s work had a very positive impact on our team’s morale. As our development quality was improving, our engineers were more confident in what they were doing, allowing them to work faster and with more confidence. As a result, our releases took less time and were less stressful.
Scope your conversation intelligence pilot
Tell us which recordings are available and which output you want to test. We agree what the 30 days cover on the scoping call.
What happens after you contact us
- We reply within one business dayA senior engineer reads what you send, not a bot.
- A short scoping callWhich conversations, and the one output that would matter most.
- The scope and the number, in writingWhat the 30 days cover, what they cost, and the week we can begin.
- NDA, then kickoffSigned before any recording moves.
10Questions
Questions about the 30-day pilot
Confirm the sample, access terms and production boundary before committing to the pilot.
Our 30-day conversation intelligence pilot runs transcription and language-model processing over your recordings to produce one agreed structured output. It is a proof of concept, or POC, for production.
You receive:
- the pilot itself, running on your own data
- an honest accuracy read against examples you can check
- a scoped, costed plan for the production build
Fixed scope and fixed price, both agreed in writing before we start. That is the fixed-price model rather than time and materials.
The fee depends on the recordings in scope and the output you want to prove. We quote after the scoping call, once those are agreed.
Four to ten hours of your team's time in total, depending on the offer, is what our fixed-scope engagements ask as a set.
On this one that is:
- a technical contact to arrange access
- someone who knows the domain vocabulary well enough to tell us when the output is wrong
- a few hours of real use in week four
- a working session at the readout
Transcription, processing and the security setup around your data are ours.
NDA before any access. For the assessments and audits we work from read-only, least-privilege access, agreed with your technical contact before anything starts. A pilot runs on the same terms: read-only access to the sample in scope, nothing granted before the NDA.
We agree where transcription and processing run, what is retained and what happens to the sample after day 30 before any audio moves.
The setup addresses prompt injection, data leakage and model access under our ISO 27001:2022 certified practices.
No model is trained on your recordings as part of the pilot. We use transcription and prompting on existing models to produce the output.
Before any audio moves, we agree which transcription and model providers are used, what each retains and what its terms say about customer data.
Not necessarily. GDPR requires a lawful basis for the planned use; consent is one option, as the EDPB explains.
As controller, you are responsible for the recording notice and lawful basis. Check the recording jurisdiction, purpose and any additional conditions for sensitive data.
At scoping, we agree:
- a sample lawfully usable for the pilot
- any redaction before transfer
- a new, consented sample where that basis is required
The accuracy read records that finding and explains whether further work can close the gap.
We identify where the output holds on your audio and where it falls short. The findings show whether better prompting, a different model or cleaner recordings could make it usable, or whether the proposed output is not viable on this data.
You use those findings to decide whether to fund production after the 30 days.
Production CRM or helpdesk integration is usually scoped separately.
The pilot produces output in the format your system would consume. The build plan names and costs its integration points.
If a working integration is the thing you need to test, we agree it at scoping and build the pilot around it.
The pilot's pipeline, prompts and evaluation set carry into the production build if you proceed.
You keep the output and findings either way. If the pilot validates the value, its costed plan becomes the brief for a build inside your systems. If it does not, the findings support your decision after 30 days.
11Where to go next
Plan the next step from your pilot
Build on the evaluated conversation output, or settle the input and investment questions first. Compare the six fixed-scope offers.
Prioritize conversation analysis alongside your other AI opportunities.
Assess the infrastructure and compute costs of running the proven output.
Prepare security evidence for enterprise buyers reviewing the conversation feature.
Build a first product release for users around an agreed scope.
Stabilize the system that holds or supplies your conversation data.
Extend the pilot into voice agents or broader conversation intelligence engineering.
Connect the validated output to your CRM, helpdesk or workflow.
Develop the model when the pilot shows prompting alone cannot meet the agreed output.
Build the pipelines, storage and retention around your conversation data.