We build the AI your company runs on.

Olypsis identifies where AI can grow your business and builds custom agents to execute your playbooks.

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We’ve built for

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What we’ve built

Outbound and research systemBiobot Analytics

35 sales meetings for Biobot’s expansion into pharma

Problem: Biobot uses wastewater samples to track the spread of disease. It wanted to sell that data to pharmaceutical companies and needed help finding the right buyers and reaching them.
Solution: We built AI tools to research target companies, assess their fit, and draft messages explaining how each could use Biobot’s data. Our two-person team reviewed the research and ran the calls, emails, and LinkedIn outreach.
Outcomes: 35 meetings booked; 27 billed as qualified meetings with client-approved accounts.
$2.4M in potential sales opportunities, according to our campaign records.

Outbound and research systemPrompt Security

Finding security buyers through a reseller’s LinkedIn audience

Problem: Prompt Security helps companies manage the security risks of using AI. It needed to reach security and AI leaders at banks and financial services firms. Direct outreach was difficult: one bank’s security chief would only consider vendors through its reseller.
Solution: We used AI to review the job titles of people following a security reseller on LinkedIn and select roles relevant to Prompt Security. Alongside this list, we researched target companies and pursued introductions through partners.
Outcomes: 4,036 potential contacts selected from 8,613 profiles, based on their job titles.
Three weekly reports from the wider outreach campaign recorded two meetings booked and one tentative meeting.

Support and knowledge assistantBalancer, Solana Foundation, Binance

AI assistants for developer support in Discord

Problem: Developers building on blockchain platforms asked technical questions in Discord, including questions the documentation didn’t cover. Support teams had to answer them manually, often revisiting questions they had answered before.
Solution: We built assistants that searched each client’s documentation, code, and previous community answers to respond with sources. A dashboard let client teams review questions, correct answers, and add information the assistant could use next time.
Outcomes: Deployed in Balancer’s Discord community; used internally by Binance’s BNB Chain team.
In BNB Chain’s test of 50 technical answers, the average accuracy score was 67.9%. Half scored 90% or higher.

Token cost optimizationThesis Finance

Reducing AI costs in an investing assistant

Problem: Thesis Finance uses AI to answer questions about portfolios and stock options. Some answers required several model calls that repeatedly charged for the same background information.
Solution: We added caching so the model could reuse that information at a lower cost, and removed duplicate and unnecessary data from the options results sent to it.
Outcomes: 57% lower model cost in an offline replay of 11 recorded options requests: $2.09 to $0.90 per answer.
Options data sent to the model was 2.5–6× smaller. The savings have not been measured on live traffic.

Client names appear with permission. Companies named as met were prospects we approached on a client’s behalf, not clients of ours.All case studies

What we do

Outbound and research pipelines: Research pipelines: a series of agent workflows that gather data at scale and filter it to what a decision needs. Prospects worth a call, competitors in a market, buying signals in filings and papers.
Support and knowledge assistants: Answer engines over your own documentation, code or past tickets, inside Slack, Teams or WhatsApp. Every answer citing its source.
Invoice and document intake: Invoices, orders and carrier notices that arrive by email and PDF, read into the system you already run. The fields pulled out and matched against the purchase order or the contract. The mismatches sent to a person with the reason. Nothing posts without approval.
Audits of AI already running: We measure the AI system you already run against your real questions and records. Then we tell you what to fix, in order.
Token cost optimization: We audit the AI systems you already run, benchmark what they spend on tokens, and put in the fixes that cut it: smaller prompts, cheaper models where they hold up, caching, tool-call monitoring, fewer calls per task. Measured on a replay of your own turns before the fix. Then on live traffic after.
Custom builds: An agent, a pipeline or an internal tool that fits no category above, wired into the data and tools you already run. Scoped from an audit and built in weekly increments you can see. The same rules as everything else here: a test on your real cases before launch, a weekly report after.

How we work

Step 1

Audit

A structured audit of how work moves through your company, across teams, systems and decision points.

We sit with the people who run it and their real records. Then we find where AI can cut overhead, speed the work up, or replace manual coordination for good.

What an audit produces

Step 2

Design and build

We design and build the system that will do the work. We wire it into the tools, data and rules that already run the process. You see it grow week by week, and a person reviews every output until the numbers say it can run alone.

Step 3

Go-live

We deploy into the stack you already run and connect it to live systems. It starts doing real work in production. It sits on top of the software you have, so nothing migrates. It runs on your cloud and on the model accounts you already hold.

Ongoing

Operate and improve

After go-live we stay on. We keep improving how the system runs: tighter decision rules, fewer failures, and the next piece of work added when it is ready.

One written report every week: what moved, what is stuck, what changes next. It goes out whether or not the week produced good news.

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Qualified meetings, cumulative

Qualified meetings billed per invoice and running total
Invoice date	Billed	To date
16 Feb 2024	0	0
26 Mar 2024	4	4
7 May 2024	6	10
11 Jun 2024	7	17
12 Jul 2024	7	24
15 Aug 2024	3	27

Fig. 1qualified meetings as they were invoicedBiobot Analyticsfrom the record

Questions operators ask first

The answers we give on the first call, written down.

Anything this leaves open is a question for the first call.

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What kind of work do you take?

Six kinds. Four have case studies behind them. Outbound and research pipelines: agent workflows that gather data at scale and filter it to what a decision needs, whether prospects, competitors or buying signals, with a person reviewing the doubtful cases. Support and knowledge assistants over your own documentation and code, with sources on every answer and a correction loop your team owns. Invoice and document intake: invoices, orders and carrier notices read into your systems and matched, with the mismatches sent to a person. Audits of AI systems already running, measured on your real questions and records. Token cost optimization: the systems you already run, made cheaper. Measured on a replay of your own turns before the fix, then on live traffic. And custom builds: the agent, pipeline or internal tool a workflow needs when it fits none of those, scoped from an audit and built in weekly increments you can see.

How long does it take?

An audit of one workflow runs two to three weeks. A first system usually goes live within the following two to three months, in weekly increments your team can see. The dates go in the audit readout.

How is the price built?

From three numbers you confirm: the revenue the work touches, the wages it consumes, and what changes when it works. You see the math before you see a price. The audit is a fixed fee agreed before it starts. Builds are scoped from the audit readout.

What do you need from us?

An executive who owns the change, access to the systems and the people, and one reviewer for what we build. Most of the hours are ours. We work best with companies of 150 to 500 people. That executive should be able to name the work, the person who owns it, and the number that should move.

What happens after it goes live?

We stay on. A system that runs by itself stops improving. After the first build we operate it, fix what breaks, and find the next piece worth building. One written report every week, whether or not the week produced good news. If you would rather take it in house, we write down what the hand-over needs and help you do it.

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Tell us the function you would want AI working in. We reply within one business day with times for a call.