What is AI automation?

AI automation combines software workflows with models that can classify, extract, summarise or draft from unstructured information. Unlike a fully deterministic rule, model output can be wrong, so reliable systems include validation, logging and human review where errors would matter.

How can AI help my business?

It can help with repetitive, high-volume tasks such as document classification, drafting replies, routing enquiries, searching internal knowledge and synchronising routine records. Whether it saves time or money depends on the current process, data quality, error tolerance and staff review required; we assess those before recommending a build.

How much does AI automation cost in Nepal?

Cost depends on the workflow, connected systems, volume, data sensitivity, human-review steps and monitoring required. We map the process first, then provide a written build estimate and an estimate of provider usage costs, which remain billed to your own account.

Can you integrate AI with my website?

Yes. We can integrate assistants or workflow steps with a website, CRM, knowledge base and support tooling through documented APIs. The design defines what data may be sent to the model, which actions need approval and how the system responds when a provider is unavailable.

Do you build custom AI agents?

Yes, where a multi-step model-driven workflow is actually appropriate. We also use simpler rules and integrations when they are more reliable. Any agent that can act on business systems is built with scope limits, review paths, logs and a defined fallback rather than being left to operate unchecked.

AI automation — Nepal

AI automation builtaround your actual work.

Chatbots, workflow automation and AI agents built on top of the processes you already run. We start by mapping the work — automating a broken process only makes it fail faster.

Team mapping a business workflow with notes during a workshop — Process mapping before automation.

Process first: We map the workflow before choosing a model
A human in the loop: Review steps where mistakes would be costly
You own the keys: Provider accounts and data stay yours
Global delivery: Nepal, UK, Australia, USA and Canada

Map before automating: Map before automating
Review where it matters: Review where it matters
Your provider accounts: Your provider accounts
Fallbacks by default: Fallbacks by default

What we build

Where automation earns its place.

Automation pays off on work that is repetitive, high-volume and rule-shaped. We will tell you when a task is none of those.

Business chatbots

Assistants grounded in your own documentation, that hand over to a person when they reach the edge of what they know.

Workflow automation

Event-triggered pipelines connecting the tools you already use, so routine handoffs stop depending on someone remembering.

CRM automation

Record syncing, deduplication and lead routing, with the matching rules written down rather than buried in a tool.

Marketing systems

Lifecycle email, engagement scoring and behaviour-triggered follow-up, built on your existing data.

AI agents

Multi-step agents for tasks too open-ended to script. We are candid about which tasks genuinely need one and which do not.

Support automation

Triage, tagging and drafted replies, with confidence thresholds that route uncertain cases to your team.

Document processing

Extracting structured data from invoices, forms and contracts, with validation before anything is written downstream.

Internal tooling

Search and question answering over your own knowledge base, so staff stop hunting through shared drives.

Integration and APIs

The connective work — webhooks, queues and retries — that decides whether an automation survives contact with real volume.

Process

Eight steps, process before model.

Most failed AI projects were technically fine. They automated a process nobody had mapped, or shipped without a plan for what happens when the model is wrong.

01
Process analysis
We sit with the people doing the work and document what actually happens, including the exceptions that never made it into the official process.
02
Opportunity mapping
Which steps are repetitive and rule-shaped enough to automate, and which depend on judgement that should stay with a person.
03
System design
The workflow, the data it touches, where a model is genuinely needed, and what happens on every failure path.
04
Build
Pipelines, prompts and integrations built against your real data, with logging from the first day rather than added later.
05
Integration
Connecting the CRM, inbox, database or ticketing system the workflow depends on, including the retry and queueing behaviour.
06
Testing and tuning
Run against historical cases, measure where output is wrong, and set confidence thresholds for what escalates to a human.
07
Deployment
Released behind review steps first, widening automation only as accuracy on your own cases justifies it.
08
Monitoring
Accuracy, cost per run and escalation rate tracked over time. Model behaviour drifts, and providers change models underneath you.

The stack

What we build with.

We deliberately do not advertise specific model versions here — they change every few months, and the right one depends on your accuracy, latency and cost constraints. We will recommend a current model during scoping.

Language models

We are not tied to one provider, and we re-evaluate as new models ship.

Anthropic (Claude)
OpenAI
Google (Gemini)
Open-weight models, self-hosted

Automation platforms

For workflows that do not warrant custom code.

n8n (self-hosted)
Make
Zapier
Activepieces

Backend and data

Where the logic, state and retrieval live.

Node.js (TypeScript)
Python (FastAPI)
PostgreSQL
Vector search for retrieval

Infrastructure

Chosen to match your existing hosting rather than ours.

AWS Lambda
Vercel functions
Docker containers
Queues and scheduled jobs

Governance

Models are wrong sometimes.

Any honest account of AI automation starts here. The question is not whether a model will produce a wrong answer, but whether your workflow notices and what it does next. That is a design decision, and we treat it as one.

Data boundaries agreed first
What may be sent to a third-party model, what must stay internal, and what needs an on-premise or self-hosted option — decided before the build, not after.
Encrypted in transit and at rest
Credentials held in a secret store rather than in workflow definitions, and API access scoped to the minimum each step needs.
Every run is logged
Inputs, outputs, model version and cost recorded per run, so an incorrect result can be traced rather than guessed at.

Built into every workflow

These are not optional extras quoted separately. An automation without them is not finished work.

Confidence thresholds

Low-certainty cases route to a person

Approval steps

Human sign-off before irreversible actions

Rate and cost caps

Hard ceilings so a loop cannot run away

Defined fallbacks

A known path when the provider fails

Pricing

Scope before build cost.

Build cost depends on the process, connected systems, data sensitivity, review steps and testing required. Model usage is billed by the provider to your account; we provide a written estimate after mapping the workflow.

Focused workflow

For a first automation — usually inbound enquiries, lead routing or a support assistant.

Process mapping workshop
One automated workflow
Integration with two systems
Logging and error handling
Handover documentation

Connected workflows

Common scope

For teams automating a chain of steps across more than one department.

Multi-step workflow pipelines
Retrieval over your own documents
CRM and inbox integration
Confidence thresholds and escalation
Accuracy and cost monitoring
Staff training session
Priority support channel

Private and complex systems

For agent systems, self-hosted models or automation touching regulated data.

Self-hosted or private model options
Multi-agent task orchestration
Custom API and webhook layer
Audit logging and access control
Data residency planning
Named engineering contact

Working together

A clear delivery relationship.

We do not publish anonymous quotes or invented efficiency figures. These are the standards we hold ourselves to on every automation instead.

A mapped process first

You get the documented workflow whether or not you proceed with the build. It is useful on its own, and it is what the quote is based on.

Accuracy measured on your data

We test against your historical cases and report where the system is wrong, rather than quoting a benchmark from somewhere else.

Provider accounts stay yours

API keys, usage history and logs sit in accounts you own. Model spend is billed to you directly and never marked up.

An honest verdict

If mapping shows the process is not worth automating, or is not stable enough yet, we will say so rather than build it anyway.

Questions

AI automation FAQs.

Start here

Tell us what you want to automate.

Send the details below to open a pre-filled email. You can also reach us directly at info@softhimalaya.com.

A look at the process before any tooling is proposed
An honest view on whether it is worth automating yet
A written next step if the work is a fit

Email us directly