[REGION] · A communications start-up
Real-time translated calling and chat app
Voice calls and chat that translate between languages in near real time, combining speech recognition, machine translation, and speech synthesis.
AI automation agency
AI automation and agents that hold up in production
Agents and automated workflows that take real work off your team, with evaluations, guardrails, and human review where it matters.
What it is
An AI automation agency designs and builds software that uses large language models to complete multi-step work, such as triaging requests, extracting data from documents, updating systems of record, and drafting responses, with a human approving the steps that carry risk. Tech Insane builds these systems as production software: versioned prompts, evaluation suites, access controls, logging, and a clear fallback when the model is unsure.
Outcomes
What changes for your business
Hours returned to your team
We start with the workflows where people copy, paste, classify, and chase. Those are typically where automation pays back first.
Decisions you can audit
Every agent action is logged with its inputs, the model output, and who approved it. Compliance reviews stop being guesswork.
Quality that is measured, not assumed
We build an evaluation set from your real cases before we tune anything, so improvements are proven against data.
What we build
Typical ai automation and agents projects
Operations agents
Ticket triage, order exceptions, back-office reconciliation, and follow-ups that update your CRM or ERP through its API.
Document intelligence
Extraction and validation from invoices, contracts, forms, and reports into structured data, with confidence scores and review queues.
Internal copilots
Assistants grounded in your policies, wikis, and databases (retrieval-augmented generation) with per-user permissions.
Workflow orchestration
Event-driven pipelines that combine LLM steps with deterministic code, retries, and human-in-the-loop checkpoints.
Approach
How we deliver it
This sits inside our standard delivery process: weekly demos, a shared board, and monthly reporting.
Technology
Map the workflow and pick one measurable target (time per case, error rate, backlog).
Collect 50–200 real examples and write the evaluation set before writing prompts.
Build the thinnest end-to-end version, behind a human approval step.
Measure against the evaluation set, then widen autonomy only where accuracy is proven.
Hand over runbooks, dashboards, and a cost-per-task view your finance team can read.
Rule-based automation vs. AI agents vs. hybrid
| Rule-based (RPA, scripts) | AI agent | Hybrid (what we usually build) | |
|---|---|---|---|
| Best for | Stable, structured inputs | Unstructured, variable inputs | Real business processes with both |
| Handles edge cases | Breaks on anything new | Adapts, but can be wrong | Adapts, and escalates when unsure |
| Auditability | High | Needs deliberate logging | High, by design |
| Running cost | Low | Per-token cost | Model only where it adds value |
Experience
Related case studies
[REGION] · A government-scale education program
Government-scale edtech platform with an AI chatbot and voice AI
A large education platform serving learners at government scale, with an AI chatbot and voice AI that make help available on demand.
Two ways to engage
Dedicated product team on monthly retainer
Build with us
We design and build your product from your requirements. You get a dedicated, senior team led by our founder, working in monthly cycles against a shared roadmap.
Vetted developers on our payroll, embedded in yours
Extend your team
Senior Tech Insane engineers join your team month to month. They work in your tools, your rituals, and your codebase, while we handle employment, equipment, and continuity.
AI automation and agents: frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot answers questions in a conversation. An agent takes actions: it calls your systems through APIs, updates records, and completes multi-step tasks. Many projects need both, with the agent running behind the chat interface.
How do you stop an AI agent from making costly mistakes?
We scope permissions tightly, keep a human approval step on irreversible actions, validate model output against schemas and business rules, and measure accuracy on an evaluation set before widening autonomy.
Which language models do you use?
We are model-agnostic. We typically benchmark two or three models (for example Claude, GPT, and an open-weight model) on your evaluation set and choose on accuracy, latency, cost, and data-residency requirements.
Can the automation run inside our own cloud?
Yes. We deploy into your AWS, Azure, or GCP account when data residency or security policy requires it, and we can use models hosted in your region.
Related
Planning ai automation and agents? Let's pressure-test it.
A 30-minute strategy call with our founder. We will ask sharp questions, tell you honestly whether we are the right team, and outline how we would approach it. NDA available before the call.