Shimira ITAI workflow automation
AI reads, sorts, and drafts. People approve what matters.
Most business processes are a chain of small judgments. Which team does this belong to? Is this urgent? What's the standard reply? Does this match our policy? Each one is quick, and together they consume whole roles.
AI is well suited to those judgments and badly suited to the consequential decisions at the end of them. The systems that work draw that line deliberately: AI handles the reading, sorting, and drafting, and a person approves anything that costs money, affects a customer, or can't be undone.
What matters here
What we automate
Triage and routing
Incoming email, tickets, applications, or requests classified and sent to the right team with the right priority, instead of someone reading every one.
Drafting
First-draft replies, summaries, and reports, prepared for a person to check and send. Editing a good draft is far faster than writing from nothing.
Checking against rules
Submissions compared against your policies, with exceptions flagged and the reasoning shown.
Multi-step sequences
Processes that span several systems: read the request, look up the record, prepare the action, wait for approval, then carry it out.
Approval and audit
A clear record of what the AI proposed, who approved it, and what happened, because “the system decided” is not an acceptable answer to a customer or a regulator.
- Node.js
- TypeScript
- Python
- PostgreSQL
- OpenAI
- AWS
We've shipped in this space
Eyeon Portal
A production system where non-technical staff drive automated actions across many locations every day, with roles and a full audit trail behind every change.
What goes wrong
Where workflow automation goes wrong
Automating a broken process
Automation makes a bad process fail faster and at greater volume. If the workflow doesn't make sense manually, fix it before adding AI.
Letting AI make the expensive call
Issuing refunds, rejecting applications, sending messages to customers: these need human approval. The value is in the preparation, not the final click.
No visible reasoning
If staff can't see why something was routed or flagged, they stop trusting it and go back to doing it manually, and you've paid for a system nobody uses.
No fallback
When the AI is unavailable or unsure, work must still flow. Every automated step needs a manual path behind it.
Measuring activity instead of outcomes
“Processed 10,000 items” means nothing without accuracy. We measure how often it was right and how often a human had to step in.
How it runs
How we build it
- 01
Map the process
1–2 weeksEvery step, who does it, how long it takes, and where judgment is genuinely required. This alone usually finds steps worth removing entirely.
- 02
Feasibility on real cases
1–2 weeksTest the AI steps against historical cases with known outcomes. Measure accuracy, estimate the hours saved and the running cost.
- 03
Pilot one path
3–6 weeksOne workflow, running alongside the manual process so you can compare, with the approval screen and audit trail in place.
- 04
Roll out and widen
varies by scopeIntegrations, more paths, monitoring, and a gradual increase in what runs without review as the evidence supports it.
Timelines & pricing
Honest ranges, before you commit.
| Tier | What's included | Timeline | Typical cost |
|---|---|---|---|
| Feasibility assessment | Process map, accuracy tested on historical cases, savings and running cost estimated | 1–2 weeks | $3,000 – $8,000 |
| Single workflow | One process automated, approval screen, one or two integrations | 4–8 weeks | $15,000 – $45,000 |
| Multi-step agent | Several systems, branching logic, audit trail, monitoring | 2–4 months | $30,000 – $90,000 |
Whether it pays for itself is arithmetic: hours saved × loaded hourly cost, minus running costs. Our AI app development cost guide works through that calculation with a real example.
Before you choose
Is this process worth automating?
- It runs often enough that saved hours are material.
- The steps are consistent, even if the inputs vary.
- You have historical cases with known correct outcomes.
- Someone can approve the consequential decisions.
- A mistake would be caught before it reaches a customer, or you'll build that check.
- The process itself is sound, and doesn't need fixing first.
If the process is a mess today, we'll say so. Automating it would make it worse.
How you work with us
Three ways to engage.
Fixed price
Prototypes from $7,500
Production MVPs from $25,000
You know what you need built. We scope it, quote one price, and deliver against it, no meter running.
- One agreed price, agreed before we start
- Milestone-based payments tied to what you can see
- Change requests priced openly, never assumed
Time & materials
from $28 / hour
Blended ~$32; senior specialists to ~$45
For work that changes as you learn. You pay for the hours spent, see exactly where they go, and can steer week to week.
- Billed on real, logged hours, reviewed with you
- Reprioritise or change direction any sprint
- Start small, scale the team as it proves out
Dedicated team
from $2,800 / month
Per developer, full-time, see the rate card
Developers who work only on your product, full-time, as an extension of your team, the offshore model most of our long-term clients settle into.
- The same people, month after month
- Your tools, your standups, your roadmap
- Roughly half the loaded cost of a local hire
Full rate card and what's included on the pricing page. Or custom software for the process around it.
Questions
Before you get in touch.
Hours saved × loaded hourly cost, minus running costs. The feasibility assessment replaces the guesswork with a measured share of cases the AI handles correctly on your own historical data, so the business case rests on evidence.
Only the ones you decide it can. We recommend keeping human approval on anything that costs money, affects a customer, or can't be reversed. Routing, sorting, and drafting are where automation is safe and valuable.
A feasibility assessment is $3,000–$8,000. A single workflow runs $15,000–$45,000, and a multi-step agent across several systems $30,000–$90,000, plus a running cost per item processed.
It should be caught at the approval step, logged, and used to improve the system. Every automated path has a manual fallback, so an outage or an uncertain case never stops the work.
Yes: CRMs, ticketing tools, ERPs, email, and internal databases are the usual ones. Each integration typically adds $3,000–$10,000 depending on that system's API.
Often plain automation with one AI step is cheaper, more reliable, and easier to debug than a fully autonomous agent. We'll recommend the simplest thing that solves your problem, and say so if AI isn't needed at all.
Cost guides
Budgeting for this?
Real 2026 numbers, worked examples, and the hidden costs most quotes leave out.
How much does AI app development cost in 2026?
Build ranges by use case, a worked monthly model-cost example, RAG vs. fine-tuning, ROI maths, and the hidden costs most AI quotes skip.
Read the guide →How much does custom software development cost in 2026?
Real 2026 numbers: cost by project type, hourly rates across every major region, the hidden costs quotes leave out, and a free calculator to model your own estimate.
Read the guide →Not sure what you need yet? That's the usual starting point.
Tell us the problem in your own words. We'll scope it with you and put the plan in writing, free, and yours to keep either way.