Your Business.Your Agent.
We don't sell software. We come to you, understand your operations, and build a custom AI agent that works exactly the way your business needs.
THE NUMBERS BEHIND THE GAP
INTEGRATIONS & TECHNOLOGIES
WHY NOW
The interface is changing. Waiting has a cost.
Enterprise software used to mean typing instructions into a screen. In 2026, it means stating a goal and letting an agent plan and execute it — with a human supervising the outcome, not operating every click. Google Cloud calls this the shift from instruction-based to intent-based computing. It's already happening in production, not in a lab. — Google Cloud, AI Agent Trends 2026
Most companies are stuck on the first rung. Here's the climb:
Simple tasks
Chatbots, information retrieval, image generation.
AI agent applications
Customer service agents, creative agents that act, not just answer.
Multi-agent workflows
Orchestrated agent teams running a business process end to end.
SO Works builds Level 2 and Level 3 systems. Most off-the-shelf chatbots stop at Level 1.
Agent maturity ladder: Google Cloud, The ROI of AI in Customer Experience, 2025.
THE GAP
Why most AI pilots die
~95% of generative AI pilots show no P&L return. The reason isn't the model.
MIT Project NANDA, The GenAI Divide: State of AI in Business, 2025
It's the process.
No evaluation discipline
"You can't improve what you don't measure" — most teams skip building a test set and evaluation criteria before shipping.
Governance as an afterthought
Safety and compliance get bolted on right before launch instead of built in from day one.
Bolt-on, not embedded
The agent sits outside the tools people already use, so adoption stalls even when the model works.
Model choice, one-and-done
Teams pick a model once and never re-evaluate as needs and models both change.
It's also who builds them: MIT found AI initiatives bought from specialized vendors succeed about 67% of the time — internal builds succeed only about a third as often. Hiring a specialist isn't the expensive option; it's the one that ships. — MIT NANDA, 2025
Every stage of how we work exists to close one of these four gaps. See how →WHAT WE BUILD FOR YOU
Any agent. Built for your exact workflow.
Five archetypes, each built to your exact workflow — and each maps to the agent categories used across real enterprise deployments: customer, employee, data, and creative agents among them.
Not an invented taxonomy — these mirror the six agent use-case categories Google Cloud identifies in production: customer, employee, code, data, security, and creative agents.
STATUS
Analyzing 847 tickets...
Churn risk flagged: 3 accounts
Customer support & retention
STATUS
3 deadlines at risk — flagged
Sprint report ready for Monday
Project delivery & team alignment
STATUS
4 posts scheduled
Engagement +34% this week
Social & content marketing
STATUS
Invoice → ERP synced
Saved 14.2 hrs this week
Back-office & ops efficiency
STATUS
Anomaly detected: CAC spike
Report ready — 6 insights
Business intelligence & reporting
STATUS
Analyzing 847 tickets...
Churn risk flagged: 3 accounts
Customer support & retention
STATUS
3 deadlines at risk — flagged
Sprint report ready for Monday
Project delivery & team alignment
STATUS
4 posts scheduled
Engagement +34% this week
Social & content marketing
STATUS
Invoice → ERP synced
Saved 14.2 hrs this week
Back-office & ops efficiency
STATUS
Anomaly detected: CAC spike
Report ready — 6 insights
Business intelligence & reporting
ARIA AGENT
Customer Intelligence Agent
A fully custom agent built around your existing tools, data, and team. Not a plugin — a dedicated intelligence layer for customer support & retention.
CAPABILITIES
- Reads every support ticket
- Detects churn risk
- Auto-responds with context
- Escalates when needed
THE ENGAGEMENT MODEL
The pilot that ships.
Grounded in Google Cloud's own framework for taking generative AI from prototype to production — Discover, Ideate & Implement, Improve.
Discover
We spend time inside your operations — observing workflows, interviewing your team, and identifying the highest-leverage automation opportunity. Not every problem is an AI problem, and not every AI problem needs generative AI — we tell you honestly which is which before we scope anything.
Architect
We choose the right model for your governance needs, use case, performance requirements, and data — model selection isn't a one-time decision, so we build in room to re-evaluate as your needs and the model landscape both change.
Build
We build an evaluation harness before we build agent logic. You can't improve what you don't measure — every capability ships against a test set and explicit success criteria, not a demo that happened to work once.
Deploy
We validate against production-like conditions and harden against the risks that actually break agents in the wild before go-live — see Enterprise Trust below.
Evolve
Governance, monitoring, and improvement aren't a final step — they run continuously underneath everything above. We stay in the loop after launch.
Pilot → Scale → Own
We start with a fixed-scope pilot designed to ship into production, not die in a deck. Prove it in one workflow, then scale to the next, then it's yours to run.
WE CROSSED THE GAP OURSELVES FIRST
We didn't pitch Siemo.
We shipped it.
Siemo is our own AI-native sales agent — handling B2B lead discovery, personalized outreach, and pipeline automation autonomously, in production, every day. We built the evaluation harness, hardened it, deployed it, and kept improving it — the same process we bring to your engagement.
Need sales automation specifically? Siemo is the product. SO Works builds everything else.
Explore Siemo → siemoapp.comENTERPRISE TRUST
No black boxes. No lock-in. No surprises at go-live.
You own everything
You own the code, the weights, and the data. We hand over a working repository, not a black box — your engineers can read it, extend it, and run it without us the day the engagement ends.
Governance as a continuous rail
Governance isn't a step we do at the end. It runs underneath every stage of our process, from the first workflow interview to post-launch monitoring — the same principle Google Cloud's own production framework insists on.
Risk-hardened before go-live
Before anything reaches production, we harden against the failure modes that actually break agents in the wild:
- recitation
- hallucination
- prompt injection
- training-data poisoning
Built toward standards you already trust
We build toward the governance and compliance frameworks enterprise security and legal teams already recognize: ISO 42001, NIST AI-RMF, GDPR, and the EU AI Act. Frameworks we build toward — not certifications we claim.
For IT: your infrastructure, your access controls, your audit trail.
For Legal: your data never trains anyone else's model.
For Finance: fixed-scope pilot, a go/no-go checkpoint before you scale spend.
For the sponsor: a working system in weeks, not a deck.
WHY SO WORKS
Battle-tested
We built and operated Siemo in production before offering this to clients — the same evaluation-driven process, proven on our own product first, not a client's dime.
No black boxes
You own the source code, the model weights, and the data. We document the architecture so your own engineers could take over tomorrow — see Enterprise Trust for exactly what that means.
Your stack, your rules
We integrate with what you already run. No forced migrations, no proprietary lock-in — the architectural openness that actually neutralizes the vendor lock-in enterprises worry about.
Istanbul-based, globally capable
Embedded in the Istanbul tech ecosystem, delivering to clients internationally — local responsiveness, global delivery standard.
Built for the team that has to defend this decision to IT, Finance, Legal, and the sponsor who signs it.
Ready to close
your own gap?
No pitch. We map your workflow in one call and tell you honestly whether an agent makes sense for your business — and if it does, what a pilot that actually ships would look like.