Six ways AI actually earns its place.
We don’t sell “AI transformation.” We ship small, specific systems that remove friction from the work your team
already does then we measure whether they helped. If they didn’t, we turn them off.
Custom AI
agents
Goal-driven assistants that can read a ticket, query your CRM, draft a response, and route it for review. Built for one job, evaluated like software, not demoed like magic.
Workflow
automation
End-to-end automation across the apps you already pay for. Trigger from Slack, read from Salesforce, write to Notion, post to Linear with an LLM doing the thinking in between.
RAG & knowledge assistants
Private retrieval systems trained on your contracts, docs, tickets, codebase. Answers cite their sources. No hallucinated policies. No “based on my training data” disclaimers.
Document intelligence
Pull structured data out of PDFs, invoices, contracts and emails. Validate against your schema. Hand only the edge cases to a human. The other 80% just… happens.
LLM
integration
Bring AI into the products and tools your team already lives in. Private model routing, prompt management, telemetry, cost controls the unglamorous plumbing that makes it work in production.
AI readiness
audit
Two weeks. We sit with your team, map the work, find the three highest-ROI automations and the three to never attempt. You get a build plan and a clear “no.” If you want.
GYMBAIT.AI
Unleash your potential with bespoke Artificial Intelligence Training. GymBAIT flipped the script by using AI to predict engagement gaps, adapt routines to individual goals, and offer encouragement when users lag.
Ask first. Build second.
Every Roars engagement starts with the same question: does this actually help someone? If it doesn’t, we tell you before we take the brief. If it does, here’s how we get from idea to in-production.
Ask the right question
We sit with your team and map the work. Not the org chart — the actual hour-by-hour. We’re looking for friction, repetition, and the moments where a person is doing what a system should.
Map the
system
Data flows, decision points, edge cases, human-in-the-loop gates. We draw the whole thing before we touch a model. You sign off on a diagram, not a vibe.
Build &
evaluate
Working pilot inside your stack. Real data, real users, real tests. We write evals before we write prompts. Nothing ships without measurable proof it’s better than the status quo.
Measure & hand off
We instrument every decision the system makes. You see what it cost, what it saved, what it got wrong. We hand off runbooks and train your team to own it. Or we stay on retainer. Your call.
Week 1 — Discovery
Week 2 — Architecture
Weeks 3–6 — Pilot
Weeks 7+ — Production
Twenty years of building software taught us one thing about new tools: the question matters more than the technology.
Six places AI earned its keep.
A few illustrative engagements named industries, real outcomes. Every number below comes from production telemetry, not a launch deck.
Contract triage & clause extraction
Vision-LLM pipeline extracts 38 clause types from inbound PDFs. Confidence-scored; anything under 0.92 routes to a paralegal. Audit log per decision, exportable for compliance.
First-response draft agent
Reads ticket, pulls customer context from CRM and product analytics. Drafts a reply with cited docs; agent edits and sends. Never auto-sends. The human is always the last keystroke.
Product copy at catalog scale
Generates titles, descriptions and metadata for 60k SKUs. Tone-of-voice fine-tuning on the brand’s top-100 sellers. Approval queue for new categories; auto-publish for refreshes.
Private knowledge assistant
RAG over SOPs, clinical guidelines and 4 years of Slack archives. Every answer cites the source paragraph. No source no answer. Deployed inside the VPC. Zero data leaves the perimeter.
Inbox to system-of-record
Reads supplier emails, extracts POs, reconciles against ERP. Flags discrepancies; auto-resolves the obvious ones. Replaces a spreadsheet, two macros and a Tuesday meeting.
Research copilot for editors
Searches archives, pulls quotes with provenance, drafts angles. Editor stays in control agent is a faster intern, not a byline. Plagiarism & bias checks built into the pipeline.
Twenty years.
Plenty of receipts.
Roars has been shipping production software since 2005 long before large language models, but with the same question driving every decision. AI is the new tool. The discipline is older.
Building software with one stubborn question in the room.
0
Yrs
AI & automation systems running for clients across 9 industries.
0
+
Weeks. The shortest distance between “what if” and a working pilot.
No retainer, no engagement, no goodbye fee. We tell you before you sign.
$
0
Frequently asked
The honest answers.
The questions we get most often, answered in fewer words than a sales call. If yours isn’t here, ask it directly we’d rather have the conversation than guess at it.
What is AI automation, and how is it different from regular automation?
Traditional automation follows fixed rules — if X, then Y. AI automation handles the messy, judgement-heavy work in between: reading unstructured documents, drafting responses, deciding which path to take, summarising context for a human. We use large language models, retrieval systems and orchestration tools to extend automation into work that used to require a person to think for thirty seconds.
Which AI automation services does Roars actually offer?
We design and build custom AI agents, workflow automation across your existing stack, RAG-powered internal assistants, document and email triage systems, AI-augmented support, and integrations between LLMs and the tools your team already uses (CRM, ERP, ticketing, data warehouses). We also run AI readiness audits for teams that aren’t sure where to start.
How long does an AI automation project take?
A focused pilot ships in 3 to 6 weeks. A production-grade rollout with evaluations, guardrails and human-in-the-loop where it matters typically takes 8 to 14 weeks. We don’t sell year-long engagements that hide the bill. You’ll see value inside the first month, or we tell you to stop.
Do you work with our existing tools, or do we have to rebuild?
We work with what you have. Salesforce, HubSpot, Notion, Slack, Zendesk, Snowflake, custom internal tools we integrate, we don’t replace. The whole point of automation is that the work happens where the work already lives.
How do you handle data privacy and security?
Your data stays your data. We default to private deployments (Azure OpenAI, AWS Bedrock, self-hosted models) and never train foundation models on your information. SOC 2-aligned practices, role-based access, full audit logs, and a clear data-flow diagram before a single line of code is written.
Will AI automation replace our team?
Not how we build it. Every Roars system has a clear seam between what the AI does and where a human owns the decision. Our best engagements give teams back their afternoons, not their jobs. If a client’s goal is purely headcount reduction, we’re probably not the right agency.
How do you know an automation is actually working?
We write evaluations before we write prompts. Every decision the system makes is logged, sampled, and graded. You get a dashboard with the metrics that matter to your business not vanity numbers like “tasks completed.” If quality drifts, we get paged. If it doesn’t improve on the baseline, we turn it off.
Bring us your least favourite part of the week.
A 30-minute call. We listen, ask questions, and tell you whether AI automation is the right answer or whether you’d be better off fixing a process first. Either way, you leave with a clearer head and zero pressure to engage.
