Anbesa

Built to excel. Built to last.

Companies grow faster than the systems that run them, and the gap costs time and money. We build the software and the operating practices that close it, and we measure the engagement by what changes in the business.

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Who we are

Our lion is our standard.

“Anbesa” means lion in Tigrinya. We took the name because the work we want asks for some nerve: telling a client the answer they did not want, and then staying to build it.

What we do

We build it. You own it.

Most software rents you your own operation. You pay every month. You pay again when you add a crew. The day you stop paying, you lose your own records.

We do it the other way around. We build the system, configure it around how you actually work, install it, and hand it over. Two services, same principle.

Service 01

Accountability Systems

Know the work got done. Prove it without making a single phone call.

Your crews are spread across sites you can't be at. Work gets marked complete that isn't. A client calls to complain and all you have is your guy's word against theirs. A payroll dispute comes down to whose memory is better.

We build you a system that records what actually happened. Shifts get published and claimed. Tasks get checked off against the shift they belong to. Every change lands in an activity trail with a name and a time on it. It's configured around your operation, not a template you have to bend yourself around.

Installed in under a week. We train your supervisors on it before we leave.

  • Shift check-in and check-out — who claimed the shift, when they started it, and when they closed it

  • Task completion on the record — every job, every visit, checked off against the shift it belongs to

  • Live crew and job status — who is working right now and what's finished, without calling anyone

  • Coverage gaps you can see — the unclaimed shift and the open assignment surface while you can still find cover

  • Records that hold up — a searchable history of every change, with the name and the time attached

  • Built for your operation — your job types, your crew structure, your own workflow states

Who it's for

ConstructionLandscapingJanitorialWarehouseTrades

One fee. Then it's yours.

A single implementation fee between $15,000 and $25,000, depending on how complex your operation is. Most land around $20,000. After that, $150 a year covers maintenance. The system runs on your own cloud account, so you pay the provider directly for hosting. That line is small at this scale, and it stays yours whatever happens to us.

You own the system. Not a license to use it. The system. Add crews without adding cost. Nobody can raise your rate, sunset your plan, or hold your records hostage.

You don't have to pay it all at once.

Pay in full, or split the implementation fee into monthly installments. Six months is the usual shape. No interest, no financing company, no credit check. The system goes live at the start either way, not when the last payment clears.

Three-year cost of a comparable platform against Anbesa
Line itemA comparable platformAnbesa
One-time implementation$2,000 – $10,000Included
Year 1 subscription$14,400None
Year 2$14,400$150
Year 3$14,400$150
Three-year total$45,200 – $53,200$20,300
Cost of adding crewGoes upDoesn't move
Who owns itThey doYou do
If you stop payingYou lose your recordsNothing happens
Based on $1,200/month, mid-range for a 20–60 person field operation on a full platform. Janitorial Manager publishes a floor of $1,000/month. Implementation fees at this tier are commonly quoted separately at $2,000–$10,000. Neither column includes cloud hosting: ours runs on your own account and bills to you directly, which is a small fraction of either figure.

You're ahead by the middle of year two. After that we cost $150 a year, and you still own the system.

Service 02

Small Language Models

A model trained on your own documents, running inside your own network.

Your people have information they are not allowed to paste into ChatGPT. Patient records. Case files. Client drawings. Underwriting documents. Contracts you signed an NDA over.

So one of two things is happening right now. Either they're pasting it in anyway, and you have a problem you don't know about yet. Or they're not using AI at all, and your knowledge sits in a filing system nobody can search.

We build small language models. Trained on your documents, deployed inside your network or your own cloud account. Your material stays in the boundary it's already in.

Ask it what you already know but can't find.

Your team asks a question in plain language and gets an answer drawn from your own material, with a citation back to the source document.

What did we agree to on the retaining wall detail?

An answer with the page reference on it, instead of forty minutes and three phone calls.

It doesn't just answer. It works.

Answering questions is where most people stop thinking about AI. It's the least interesting thing a model does.

The real value is that a language model can be dropped into the middle of a process as the step that reads something and decides what it means. That's the step you've never been able to automate. Rules-based software can move a file, check a box, and send an email. It can't read a paragraph and understand it. So every process that depends on someone reading something still depends on a person, and that person is the bottleneck.

A model handles that step. Which means the whole process can finally run without waiting on anyone.

What that looks like in practice

It reads incoming documents and pulls out what matters.

An invoice, a referral fax, a rate confirmation, a purchase order. The model extracts the fields, checks them against your records, and either posts it or flags the mismatch. What used to be a person retyping data becomes a queue of exceptions.

It routes and prioritizes.

Every RFI, ticket, complaint, and request gets read, classified, and tagged with urgency. It reaches the right person before anyone opens an inbox.

It drafts from your own records.

Service reports, submittal responses, incident write-ups, intake summaries, first-pass contract redlines. Not a blank page. A draft built from your data, in your format, ready for someone to check and send.

It checks things against other things.

Submittal against specification. Invoice against contract. File against completeness checklist. The model reads both and tells you what doesn't match, on every document, not just the ones somebody had time to review.

It summarizes at volume.

The overnight digest. The shift handoff. The weekly rollup of everything that came in. Written every day without anyone writing it.

It runs multi-step workflows end to end.

Read the document, decide what it is, pull the related record, draft the response, route it for approval, and file it once approved. The model handles each judgment call in the chain. When it isn't confident enough it escalates to a person instead of guessing.

The part that makes automation affordable

This is the part that changes the arithmetic. When you're paying a per-question fee to an AI vendor, you automate carefully. You pick the high-value documents. You watch the meter. Automation becomes a budgeting exercise.

When the model runs on hardware you own, the cost per run is effectively zero. So you stop choosing. You run it on every invoice, every email, every night, every file in the archive. That includes the low-value work nobody would pay per question to process, which is exactly where the wasted hours live.

That's the difference between AI as a tool a few people open, and AI as something running quietly underneath your operation all day.

A human is still in the loop

Every workflow we build has a confidence threshold and an escalation path. When the model is sure, it acts. When it isn't, it stops and hands the item to a person with its reasoning attached. You decide where that line sits, and you can move it as trust builds.

We'd rather build something you turn up over six months than something that oversteps in week one.

"Small" is the point.

Small doesn't mean weak. A model trained on ten years of your project specifications beats a general-purpose model on your questions. The big one has never seen your documents. The small one is faster, costs a fraction per question, and runs on hardware in a rack in your own building.

You're not trying to build something that writes poetry. You're trying to answer what did we agree to without sending out a search party, and process four hundred invoices before anyone gets in.

  • Trained on your corpus — your specs, records, contracts, manuals, correspondence

  • Deployed where you say — on-premises hardware or your own private cloud account

  • Cited answers — every response points back to the document it came from

  • Workflow automation — the model as a step inside your processes, not a chat window off to the side

  • Connected to your systems — reads from and writes to the software you already run

  • Human escalation built in — confidence thresholds you control

  • No data egress — deployed inside your boundary, with no outbound call to a model vendor

  • Access-aware — the model respects the permissions your people already have

  • Full audit log — who asked what, when, what came back, and what the system did about it

Who it's for

Organizations sitting on large amounts of proprietary data that legally or commercially cannot leave: healthcare, engineering and construction, energy, financial services, government contractors, legal, logistics, research.

Start small. Prove it works. Then scale.

Nobody should commit a large budget to AI on the strength of a demo. So we don't ask you to. Every engagement starts with an audit, and each stage earns the right to the next.

AI Readiness & Data Audit
We inventory your data, rank use cases by return, model the ROI, and design the security architecture. You get a written recommendation whether or not you continue with us.
Proof of Concept
One narrow use case, your real data, your environment, scored against a test set you help write.
Departmental pilot
Real users, real workflow, one department.
Production deployment
Hardened, integrated, monitored, supported.
Managed program
Multiple models, ongoing retraining, governance.

Every stage is scoped, priced, and dated against your operation before it starts. We won't quote a schedule off a website table. You'll have a firm timeline in writing before you commit to anything.

Engagements past the audit can be split into monthly installments across the term of the work. We'll put the schedule in the statement of work before you sign it.

The first three audits are free.

We're new to this market, and we'd rather have three clients who will talk about us than three invoices. The audit normally runs $12,000. For the first three organizations we'll waive it. In exchange we ask for three things. A named executive sponsor, and access to real data rather than a sanitized sample. Then your name and a reference call, once we've delivered.

Same scope. Same rigor. Same written recommendation. After three, the fee applies.

Short engagements. Written scope. No surprises.

We scope it before we quote it

You get a fixed price and a fixed deliverable, in writing, before any work starts. If scope changes, we tell you and you decide. You don't find out on the invoice.

We install fast

Accountability systems go live in under a week. We'd rather your team be using it than be in another meeting about it.

We hand it over

Training for your people, documentation for your records, and a system that keeps working whether or not you ever call us again.

The firm

Boardroom discipline. Builder's speed.

Most firms give you one or the other: consultants who plan or developers who build. Anbesa puts both in the same room, on the same team, accountable for the same outcome.

Work with the firm

Integrity

Vendor-neutral advice, transparent pricing, and the truth even when it is smaller than the sale.

Expertise

The people who scope the work are the people who build it. Nothing gets promised by someone who won't have to ship it.

Unwavering support

We train your team, answer the phone, and stay until the work works.

Courage

We will give you the recommendation you did not want, and disagree with you in the room rather than after.

How we work

From the first conversation to the running system.

  1. The Strategy Consult

    We listen, map the operation, and find where time and money leak.

  2. The Blueprint

    Scope, price, expected return, and plan in plain language.

  3. The Build

    Working systems early and often, with weekly visibility.

  4. The Run

    Training, adoption, and care until the system becomes the work.

FAQ

Before we start.

Where does an engagement begin?

Usually with a short diagnostic on one decision or one broken workflow. We interview the people closest to the work and look at what the systems actually record. Then we hand back a ranked plan with owners, dependencies, and a first thing to build.

Do you advise, implement, or both?

Both. The same team frames the decision, designs the workflow, and stays accountable through build, adoption, and handoff. Nobody here writes a strategy deck they will not have to deliver against.

Can you work inside our cloud and compliance boundary?

Yes. We work inside your tenancy, your network, your identity model, and your approved service catalog, on whichever cloud you already run. That can include managed model endpoints, a private search index, private network connectivity, and client-managed keys. Your data does not need to leave your boundary.

What happens to our data?

It stays yours. We use least-privilege access, document provenance and retention, and keep client data segregated. We do not use client data to train a shared model or create an asset for another client.

How do you measure success?

We baseline the number the work is meant to move: cycle time, conversion, cost to serve, reliability, decision quality. Delivery, adoption, model quality and platform health are how we track progress. The business number is the one we are judged on.

Will our team be able to own what you deliver?

Yes. Documentation, training, and operating practice are part of the delivery, not a phase we bill for afterwards. The goal is a team that no longer needs us.