We build the AI your company runs on.

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

Book a call Read the case studies

We’ve built for

(opens in a new tab)

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. We agree on any integration or migration work before launch. The deployment environment and model accounts are agreed with your team.

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.

Book a call

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.

Book a call

Do I need to know exactly what to build?

No. Bring a process you’d like to improve, a task that takes too much time, or an idea you want to explore. We’ll work through it with you and recommend a starting point.

Do we need a technical team?

We handle the engineering. We’ll need someone who knows the process to answer questions and review what we build, plus help from whoever manages access to your software. We agree on your team’s involvement before work starts.

Can you work with our existing software?

Yes. We start by checking how your current tools store and share information. That tells us what we can connect to, what access we need, and whether any changes are required. Those details go into the proposal.

How long does a project take?

The schedule depends on the scope, data, access to your systems, and testing. We agree on the stages and target dates before starting, and show you progress as we build.

How do you price a project?

We agree on the audit scope and fee in advance. For a build, we put the scope, timeline, and price in a written proposal. Ongoing support and the costs of hosting, AI models, and other services are set out separately so you can budget for running the system too.

Who owns the software and data?

Your data remains yours. We set out ownership of the custom code, any third-party licenses, and access to the running system in the agreement before work starts. If your team will take over, we include the documentation and training they’ll need in the scope.

What happens to our data?

We agree on what data the system needs, where it will run, and who can access it. Your IT or security team helps us check how that data is stored and shared with AI providers. We settle this before connecting to your systems.

What happens after launch?

We monitor the system, fix issues, and improve it under an agreed support plan. You get a weekly update on results and planned changes. Before launch, we agree on how to report problems and when you can expect a response. We can also help your team take over if you want to run it yourself.

Book a call

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