What we do, in one sentence
Most AI consultants ship a slide deck. We ship a running agent doing one specific job, with a kill switch the operator controls, an audit log a regulator can read, and a measurable hour count it took back. By week eight the agent is in production on the workflow that was costing the team the most hours. We pick the workflow first, name the operator who keeps override second, write a one-page scope third. You have a real opinion from us inside thirty minutes of the first call, not three weeks.
Who we work with
B2B teams of roughly 50 to 500 people, in any industry. We have shipped against the same shapes in logistics, healthcare, legal, banking, retail, manufacturing, insurance, real estate, recruitment, professional services, education, energy, telecoms, hospitality, construction, pharma, food and restaurants, automotive, agriculture, and non-profit. The 50 to 500 band matters more than the sector. Teams smaller than 50 usually have no workflow that fits the agent-tractable pattern yet. Teams larger than 500 often have an internal AI group already, and our role is best as an accelerator. Agent patterns are industry-agnostic. What changes is the constraint each industry carries: HIPAA Minimum Necessary in healthcare, the auditor's reconstruction test in banking, line-down risk in manufacturing, the FNOL queue at 6am on a Monday in insurance. We adapt the patterns; the patterns themselves repeat.
What we believe
Agents that ship are scoped to one workflow
The single most common cause of failed AI projects is overscoping. The buyer wants "AI-powered operations". The realistic deliverable is one agent replacing one workflow: invoice reconciliation between Salesforce and the ERP, lease abstraction for a property team, prior-auth packet assembly for a clinic, FNOL triage for an insurer. We build against actual production integrations, not stubs, and cut over with rollback hooks. The second workflow comes after the first one is paying back, not before. A planner at a UK 3PL with eleven browser tabs open is not a feature request, it is a scope.
The operator keeps the override authority
Every agent we ship has a written escalation policy and a kill switch the operating team controls. The model is the engine. Escalation is the architecture. On a recent insurer build, the head of claims kept one button on their dashboard that halted the agent and routed the queue back to manual in under ten seconds. They used it twice in the first month. That is the design. We will not build an agent that takes irreversible action without a human in the loop on day one.
The audit log is the deliverable, not the model
Eighteen months from now an auditor will ask why the agent closed a particular case on a Tuesday in March. The honest answer needs to take thirty seconds, not three weeks. We instrument every decision with the full inputs the agent saw, the model and prompt version, the confidence score, the escalation rules that applied, the decision rationale, the downstream actions, and timestamps. The audit log is what makes the agent defensible. Defensible matters more than clever.
We say no when it is the honest answer
Roughly seven in ten AI ideas we are pitched die at our four-question filter. The most common kill reason: the data sits behind a vendor product with no API, no integration, and no realistic way to scrape. A property team once asked us to build against a lease management tool reachable only through a Citrix-published Windows client. We said so on the call. Screen-scraping is a maintenance bill that arrives every six months for the rest of the project's life.
How an engagement works
A first conversation is fifteen to thirty minutes, no slides. We want to understand the workflow: what it is, what systems are involved (Salesforce, Guidewire, Epic, ServiceNow, SAP, the homegrown thing nobody wants to touch), who owns it, what the binding constraint is. By the end of the call you have a useful first answer: this is or is not an agent problem, here is the likely shape. If it is a fit, the next step is a scoped build proposal on a page. Read the full methodology for the four-phase build pattern.
What we will not do
- Pure pilots with no production owner. A pilot without a named operator and a written go/no-go gate gets extended quietly to keep the project alive and dies in month seven. We covered the failure mode in this article.
- Agents on workflows with no API access. If the data sits in a vendor product reachable only by a person typing into a screen, we will tell you on the first call. Screen-scraping breaks the first time the vendor ships a UI update, which they will, on a Wednesday, with no warning.
- Production agents without a named operator. If no human has the time or authority to keep override, the build will quietly fail in month four. We have watched it happen. We surface this constraint before the contract.
- Multi-agent systems on a first build. Two agents talking to each other is twice the failure surface and half the audit clarity. The first build is one agent. Always. Reasoning here.
Who is behind Synarsi
Shruthi Kanthayapalem
Managing Director & CEO
Shruthi Kanthayapalem is Managing Director and CEO of Synarsi. She leads the consultancy end to end: client engagements, agent delivery, operations, and the team. Every project ships a production AI agent on a named workflow, with a written escalation policy, an audit log, and a rollback plan from day one. She works out of Hyderabad and is on LinkedIn at linkedin.com/in/shruthi-kan. If you have an operations workflow worth talking about, that is the right place to reach her.
Get in touch
If you have a workflow that costs your team hours every week and you can describe it in two sentences, send us a message. Bring the workflow name, the systems it touches, and a rough hours-per-week number. That is enough. We reply within one business day with a useful first answer. No deck, no pressure, no "discovery call" required to get to a real opinion.