The 90-day path from LLM idea to production
Agentic AI workflows your team owns when we leave.
We help businesses figure out where AI and LLMs actually fit, then build the workflow into your stack with evals, guardrails, and a human-in-the-loop path from sprint one. No black boxes. No vendor lock-in.
In plain English
An agentic workflow is a smart intern that knows your tools and when to ask for help.
Imagine giving a new hire a task: "handle this refund." A good one knows which of your tools to use (CRM, knowledge base, ticket system), remembers what they have already done, and brings it to you when they are unsure. That is exactly what an agentic workflow does — in software, at scale, with full audit logs.
What it is not
- ✕
A chatbot
A chatbot answers one question at a time. An agent runs a multi-step workflow.
- ✕
A Zapier flow
A fixed automation always follows the same path. An agent decides each next step.
- ✕
A magic black box
It is software you own. Every decision the agent makes is logged and auditable.
Inside the loop
The whole thing in one picture.
Five small steps. The agent reads a task, picks a tool, remembers the result, checks its own confidence, and either finishes or hands off to a human. That loop repeats until the workflow is done.
Operator gives a task
"Handle this refund." Plain language. No special syntax.
A loop that decides what to do next.
Four small parts. Each does one job.
Plans the next step
Calls your CRM, KB, ticketing
Remembers what is done
Scores its own confidence
03 · Loops back when more work is needed
Done
Action shipped. Audit log written.
Human reviewer
The agent stops, summarises, and asks.
Operator gives a task
"Handle this refund."
A loop that decides what to do next.
Four small parts. Each does one job.
Plans the next step
Calls your CRM, KB, ticketing
Remembers what is done
Scores its own confidence
03 · Loops back when more work is needed
Done
Action shipped. Audit log written.
Human reviewer
The agent stops, summarises, and asks.
Loops back automatically when more steps are needed. Stops the moment a human should be in the room.
The 90-day path
Strategy to production in three blocks, with a gate at each end.
No 12-month transformation. No vendor PowerPoint. A small, paid audit kicks it off; a workflow your team owns lands it.
- Week 101
Strategy audit
Map your workflows. Score AI candidates by ROI vs risk. Pick one.
- Weeks 2 to 402
Design + eval harness
Agent graph, tools, memory, evaluators. 50-case offline harness in CI.
- Weeks 5 to 1003
Build + integrate
Production build on your cloud with observability and a human-in-the-loop interface.
- Weeks 11 to 1304
Operate + measure
Weekly working-session demos against real cases. Tune evals and prompts.
What we build for you
A real workflow. Not a demo.
Every engagement ships on your cloud, in your stack, with the rails your team can keep running long after we leave.
A one-week AI strategy audit with a ranked, ROI-scored backlog of workflows to automate.
Production agentic workflows built with LangGraph, LangChain, or the Vercel AI SDK on your cloud.
Offline eval harness and online observability so regressions are caught before customers feel them.
Human-in-the-loop interfaces for approvals, escalations, and edge-case overrides.
Runbooks and ownership handover so your team operates the workflow after launch.
Toolchain we use
Mature pieces. Not a moonshot stack.
We pick boring, proven libraries and use them well. The audit recommends a stack that fits your existing infrastructure, not the most exciting one.
Engagement models
Three ways to work with us.
AI strategy audit
One-week paid assessment with a ranked, ROI-scored backlog of workflows to automate.
Workflow build
4 to 10 week project to ship one production agentic workflow end-to-end, with evals, guardrails, and runbooks.
Embedded AI pod
Dedicated forward-deployed engineer plus QA and security on a monthly retainer for continuous AI delivery.
Why Hashorn
Senior engineers who have shipped agents, not just demoed them.
Senior engineers who have shipped agents into production, not just built demos.
We tell you what NOT to automate. Saying no is part of the audit.
Evals, guardrails, and human-in-the-loop are sprint-one work, not a launch checklist.
Your team owns the workflow after the engagement. No vendor lock-in.
Frequently asked
Questions buyers ask us in the first call.
How is this different from buying an off-the-shelf AI tool?
Off-the-shelf tools handle the generic 60% of a workflow. Hashorn builds the specific 40% that connects to your data, your operators, and your guardrails. The result is a workflow your team owns and can extend, not a vendor you renew every year.
How do you decide which workflow to automate first?
The strategy audit scores candidates on three axes: business value (time saved or revenue protected), feasibility (data quality and tool coverage), and risk (regulatory, customer-facing, or reputational). The first workflow we build is the highest-value low-risk one, not the most exciting demo.
What does human-in-the-loop actually mean in production?
An approval queue for high-stakes actions, confidence thresholds that route uncertain cases to a human reviewer, full audit logs of every agent decision, and an interface your operators actually want to use. We design these as first-class surfaces, not afterthoughts.
Do we need to be on a specific cloud or LLM provider?
No. We deploy on AWS, GCP, or Azure, and we work with OpenAI, Anthropic, AWS Bedrock, Google Vertex, and self-hosted models. The audit's first job is to recommend a stack that fits your existing infrastructure, not force a new one.
Ready when you are
Book an AI strategy audit.
One paid week. A ranked, scored backlog of workflows to automate. Honest reasoning, including the workflows we recommend you do not automate.