TGH Tech
Bleno · Workflow intelligence platform

One person knows how it’s done. Now everyone does.

The way your best people work, held by the company instead of in their heads.

Most AI tools hand one person a chat box and leave the rest to them. Bleno holds how your company does each job: the standards, the material, and the order the work is actually done in. Anyone in the role can run it and get the same answer back. You pick which model does which job, what it is allowed to see, and what it costs.

Days to hours One standard Every cost in view bleno.io
Start with three jobs Visit bleno.io
01 · The problem

The problem is not that your people cannot use AI. It is that your organisation has no way of working with it.

Capability stays with individuals. Method stays in heads. Nothing compounds.

01

Work only two people can do

A job takes days and one or two senior people are the only ones who can do it. When they are busy, it waits. When they leave, it stops.

02

Methods that live in heads

The way this company prices a job, checks a drawing, chases a payment or scopes a programme is not written down anywhere.

03

Quality that swings by person

Two people do the same task and produce different standards of output. Nobody can say which one was right.

04

AI use nobody can see

Different people, different tools, different accounts. No view of cost, no view of quality, and no record of what data left the building.

02 · What Bleno is

Define the job once. Everyone runs the same one.

In most companies, AI capability sits with whoever is good at prompting. The output quality changes person to person. The methods stay in individual heads. Nobody knows what is being spent or what data is going where.

You write down how a job should be done, once. That description carries your context, your methods, your standard and your limits. Anyone who holds the role gets it automatically, without setting anything up themselves.

Underneath, the organisation stays in control. Each task is routed to the model that clears the quality bar at the lowest cost. Data boundaries, permissions, budgets and audit are set by the company, not by the tool.

03 · Why Bleno is different

A chat box serves a person. Bleno serves the company.

DimensionGeneric AI toolsBleno
Who it servesIndividualsThe organisation
FlowUser asks, model answersOrganisation defines the work, role carries the capability, person executes the workflow
ContextThe user supplies it every timeBuilt into the system
QualityDepends on prompting skillSet by role and workflow standards
CapabilityStays with the individualAttached to the role, inherited by whoever holds it
ModelsWhatever the tool gives youYou choose per task, by quality and cost
DataLimited control over where it goesYou decide what goes to which model, when and how
CostPer seat, no view of usageBudgets, quotas and per-workflow visibility
From AI access
to a work system

Bleno organises work, intelligence and execution, not just access to a model.

From prompt skill
to role capability

Capability is attached to roles, so everyone works to the same standard.

From one-size intelligence
to task-right intelligence

Models are routed by task type, quality bar and cost.

From data upload
to data control

You decide what data goes to which model, when and how.

What exactly you get

Three named tools, and the controls to run them.

01

Three tools, one week each

For the three jobs your team repeats every week that eat the most time.

02

Built from your own material

Your context, your standards, and the sequence your best person already follows.

03

Written up for you to approve

We propose how each job should run. Nobody on your side starts from an empty field.

04

A monthly ceiling you set

Per team, so spend cannot run away between one invoice and the next.

05

Every call attributed

To the tool that made it and the person who triggered it, so “is this worth it” gets answered per team.

06

Nothing rebuilt, nothing down

It sits alongside what you already run.

04 · The three questions it answers

Three questions you already have, answered in one place.

A company defines how AI should work for it once, then runs that on whichever model is cheapest for the job. Defined once, available in chat and inside your own applications.

“What should good look like here?”

Context, what the model is given and told before anyone types.

“What should it cost?”

Costs, which model answers, inside which tier and which budget.

“Where do I get to it?”

Utilities, the jobs this company does, ready to run in chat and inside its own apps.
05 · What Bleno delivers

Ten capabilities. Five carry the weight.

The five marked below are the ones an owner buys on. The other five are what keeps it working a year later.

Workflow first

Outcome-focused execution, not generic chat. Real work, organised around what it is meant to produce.

Org to role to person

Capability is attached to roles and inherited by the people who hold them.

Context and method built in

The context, methods, guardrails and quality bar your company already has, carried into every task.

Model control

The right model for the right job, specific to role and function. You stay in control.

Cost control

Budgets, quotas and tiered quality. The cheapest model that clears the bar.

Quality and standards

Consistent output across people and teams, whoever happens to be doing the work.

Observability

Usage, quality, performance and outcome analytics, per workflow and per team.

Data and state control

You decide what data goes where, and what state is kept afterwards.

Integrations

Connect the tools, data sources and business systems you already run.

Governance

Permissions, compliance, audit, approvals and policies, set by the company.

06 · Bleno in action

One job, start to finish.

  1. 01

    Start work

    The user picks a workflow or a task.

  2. 02

    Context applied

    Organisation, role and personal context are applied automatically.

  3. 03

    Model chosen

    The best model is selected on policy, quality and cost.

  4. 04

    Execute

    The work runs inside the guardrails and to the standard set.

  5. 05

    Outcome

    Reviewed, measured, and saved back into what the company knows.

The loop closes. Every completed job improves the context, the workflows and the quality bar, so the company gets more capable rather than just faster.

07 · The premise it all rests on

The model is the engine. The harness is everything that decides what the engine is pointed at, and it is the harness, far more than the model, that determines whether the output is any good.

Two people can use the identical frontier model on the identical task and get results a grade apart. The difference is not raw intelligence. It is what surrounded the request: what context was supplied, what the model was told good looks like, which sources and tools it could reach, and what was checked before the answer came back.

This matters commercially because model access is converging. Every organisation buys the same models at roughly the same price, and the gap between the leaders narrows with each release. The harness is the opposite, specific to a company, improvable on purpose, and not on anyone’s price list. It is the one part of the AI stack a company can genuinely own.

08 · One product, two depths

Level 1 works in days. Level 2 is built around how you actually work.

Level 2 is not a bigger Level 1, it adds a dimension the foundation does not have. Switch between them.

Level 1, the foundation Ships as software · time to value in days

Three cogs, each flat. No layering, no per-role modelling, no per-person configuration. One company context applied to everything, cost control around it, and a set of utilities people actually open.

One harness

Context, one company harness

A single organisation-level harness supplying what every interaction should carry: company context, house standards, approved sources, and guardrails on what may and may not be done. One level, applied uniformly.

What it enables

Before anyone types a word, the interaction already carries what the company knows and how the company works. Intelligence is extracted against real organisational material rather than generic priors.

Who it serves

Everyone at once, configured by whoever owns standards, typically a founder or an operations lead in a company of this size.

The value

The same model, available to every competitor at the same price, produces materially better output for this company, because it is asked better and given better material. The largest single quality improvement available without changing models.

Spend control

Costs, tiers, attribution and budgets

Model tiers set by the organisation, free and open, mid, frontier, assigned to classes of user and use case. Every call attributed to the application that made it and the person who triggered it. Budgets held per team, application or tier, with visible burn.

What it enables

Spend follows task difficulty rather than brand recognition. “Is this worth it” gets answered per application and per team rather than as one invoice.

Who it serves

Whoever owns the bill, and every manager who currently cannot approve AI spending without escalating.

The value

The adoption unlock. What blocks adoption is rarely lack of interest, it is unbounded downside. Cap the downside per bucket and the organisation can push adoption harder, not more cautiously.

Named workflows

Utilities, the jobs this company does

Named, ready-to-run workflows in the chat surface, draft the renewal proposal, triage this queue, prepare the client update, plus the same capability reachable from your own applications through an embed.

What it enables

A person picks the thing they need done rather than describing it. AI features inside your own products inherit the same context, tiers, budgets and attribution as everything else.

Who it serves

Every employee in the chat surface; the engineering owner through the embed.

The value

Prompt-writing stops being the barrier between an employee and a useful result, which is what makes adoption real rather than merely reported. The embed is what turns Bleno from a tool a team uses into infrastructure a company runs on.

Level 1 is a complete, coherent product: better output than you had, control over what you spend, workflows people use. It is not, on its own, defensible, flat company context, model tiering and a workflow library exist elsewhere. Its job is to land, be useful immediately, and earn the next conversation.

09 · Why depth is safe

Set it once for the company. Every role and every person picks it up.

Inheritance is what makes configurability safe: without it, every person building their own harness dissolves the company standard. It also answers the blank-canvas problem, configuring is always an edit to something that already works, never creation from zero.

A personal harness cannot override an organisation guardrail. That is enforced, not encouraged.

Organisation harness

Company context, standards, approved sources, guardrails. This is all Level 1 has; in Level 2 it becomes the base.

Role harness

The method, tools and material a particular job needs. Specialises the base; cannot contradict it.

Personal harness

One person’s adjustments for their own work, within what they inherit.

10 · What it is not

A company brain remembers. A company OS coordinates. Bleno produces.

They answer different questions at different moments, and a company can sensibly have all three. Bleno decides how a job is done to this company’s standard, and what it costs to do it, which is the row neither of the others has at all.

Company brain

“What did we agree with this client last quarter?”

Primitive · retrieval Used · before work, as a lookup Stance · descriptive, it reports what exists
Company OS

“Where is this project, and who owns the next step?”

Primitive · state Used · around work, continuously Stance · structural, it organises work
Bleno

“Write the renewal proposal the way we write them, and don’t spend frontier money doing it.”

Primitive · method Used · during work, at the moment of production Stance · prescriptive, it encodes what good looks like
DimensionCompany brainCompany OSBleno
Question it answers What do we know? What is happening, and who owns it? How is this done here, and what should it cost?
Moment of use Before work, lookup. Around work, coordination. During work, production.
Unit of value A found answer. An organised process. A usable output, at a known cost.
Chooses the model and the spend No. No. Yes, tiers, budgets, per-app attribution.
Shape of the context Flat. Everyone queries the same index. Flat, organised by project rather than by role. Layered, organisation, role, person, with inheritance.
What breaks without it Knowledge is lost or re-found repeatedly. Work is uncoordinated. Output quality is a lottery and spend is unbounded.

If you already run a knowledge platform, Bleno treats it as an approved source. If you run a workspace platform, Bleno is reached from inside it through the embed. Most companies of this size have neither, and feel the problem as inconsistent output and an unpredictable bill.

The first step, up front

Three jobs. One week each. Your material, your standard.

Pick the three jobs your team does every week that eat the most time. We build each one as a named utility, your context, your standards, the sequence your best person follows, and you approve a proposed harness rather than authoring one from an empty field. A company of sixty has nobody whose job is populating an AI platform.

Derived from your own material Approve, don’t author Days to value One function first, then the next
11 · Architecture

Four layers, if you want them.

This is the part a technical buyer asks for. Nobody has to read it to use the product, and no owner should have to read it to decide.

Layer 1

Work experience

What people see and do.

  • Workflows, pre-defined and custom
  • Tasks and utilities, reusable building blocks
  • Chat in context
  • Knowledge access: search, retrieve, reference
  • Dashboards for work status and output
  • Notifications, alerts and reminders
  • Integrations with connected tools and systems
Layer 2

The context layer

How intelligence is structured and applied. This is the part no generic tool has.

  • Organisation: vision, policies, methods, guardrails, approved sources
  • Role: objectives, scope, processes, quality standards, templates
  • Person: preferences, working style, history, trusted sources
  • Produces: standard context, standard method, a quality bar by role
  • Outcomes: consistent output, faster onboarding, knowledge that stays
Layer 3

Intelligence control

How models are chosen and controlled.

  • Model routing by task type, complexity, quality bar and cost
  • Cost control: budgets and quotas per user, role, workflow and tier
  • Quality control: automatic checks, human review, feedback loop
  • Data and state control: context boundaries, retention rules
  • Observability: logs, tracing, performance, audit
  • Governance: permissions, policies, approvals
Layer 4

Model layer

Works with any model, and you choose.

  • OpenAI, Anthropic, Google
  • Open models such as Llama and Mistral
  • Local and on-premise models
  • Specialised domain models
  • Several can run in parallel, each task routed to the most suitable one
Deployment

Cloud, private cloud or on-premise.

State and context

You decide where context and state live, and how they flow.

Selective exposure

Not all data reaches one model. You decide what is shared.

Model segmentation

Different data and workloads go to different models, by design.

Enterprise integrations

ERP, CRM, documents, email and project tools.

APIs and extensibility

Extend Bleno with your own tools, agents and integrations.

12 · What it costs to run, kept in view

A ceiling you set, per team, so nothing runs away.

Routine work runs on cheap models, hard calls run on the best one, every use is tied to a person and a job. What blocks AI adoption is rarely lack of interest, it is unbounded downside, and nobody authorises a bill they cannot predict. Cap the downside per bucket and the organisation can push adoption harder, not more cautiously.

Tiers

Free and open, mid, frontier, assigned to classes of user and use case, so spend follows task difficulty rather than brand recognition.

Attribution

Every call attributed to the application that made it and the person who triggered it. “Is this worth it” gets answered per team, not as one invoice.

Budgets

Held per team, application or tier, with visible burn. A manager green-lights a new use case knowing the worst case is bounded by their bucket.

One month of AI work

Same work. Three ways of governing it.

Set the size of the team, then change how the work is governed. What moves is not just the bill, it is what you can answer about it afterwards.

People doing AI-assisted work
100 people
102550100250500
How the work is governed
Where the month’s work actually runs 12,000 runs
Open / cheap tier 6% Mid tier 24% Frontier 70%
Model spend this month $188 Everything defaults to the model people have heard of.
Attribution None One invoice, no idea which team or application spent it.
Quality bar Nobody’s Quality depends on who wrote the prompt that morning.
Key-person risk Per person How the work is done lives in a few strong performers.

Nothing here is anyone’s fault. Individual subscriptions, individual prompts, one invoice at the end of the month, the organisation gets whatever each person happened to do, and cannot answer a single question about it afterwards.

Indicative only, on public list prices and observed routing mixes. The point is not the number, it is which questions have an answer at each level. Your real figures come out of the first month of attribution, not this control.

13 · Value to the organisation

What the company keeps after the work is done.

  • A standard way of working with AI across the whole company
  • Higher quality work, consistently
  • Lower cost through smart model usage
  • Faster execution of real work
  • Stronger security, compliance and control

And one more that compounds: institutional knowledge becomes an asset rather than a memory.

In most companies AI use produces nothing durable. Every session starts empty. Here the method is captured once and reused by everyone who holds the role, so the company gets more capable rather than just faster.

14 · The part an owner feels rather than reads

The week stops routing through you.

In a business this size, how the work really gets done lives in three or four heads. Everything, quality, ramp time, resilience, is hostage to them. Encode the method into the system and a role performs to standard regardless of who is currently in it.

A new joiner in weeks, not months

Someone three weeks into a role operates closer to someone three years in, without shadowing your seniors.

The standard survives a resignation

It transfers on a first day rather than over a first year, and it can be improved on purpose because it exists somewhere it can be edited.

Capability the company keeps

Organisational effectiveness becomes something to invest in directly rather than something to hire for and hope to retain.

15 · Who it is for

Owner-led companies of roughly 40 to 200 people, most of them in construction and field services.

That is where the product is sharpest today, because that is where the method sitting in two or three heads does the most damage. It also works for any organisation that wants AI to run the way it already works.

Project-driven organisationsProfessional service firmsOperations-intensive teamsMid-market and enterprise
16 · Who gets value, and what kind

Four people in the building, four different reasons to care.

The employee

Opens a utility built for the job in front of them instead of a blank box, and gets a usable result without having to be good at asking.

Level 1 → deepens at Level 2
The manager

Approves a new use case inside a bucket they control, and afterwards sees whether their team got anything out of it.

Level 1
Platform & finance

Sets tiers once, reads spend broken out by application and team, and stops being surprised by the invoice.

Level 1
The founder

Owns capability as company property rather than as a set of individuals, and can point to what the AI spend bought.

Level 2
17 · What they’re already telling us

In the buyer’s own words, before we said anything.

“My team uses AI now and everything still comes back to me to fix. I’ve become the bottleneck, and I can’t tell if any of it is making us faster.”A function lead

“We’re spending real money on AI, I can’t tell what it’s buying, and I can’t let people expand it because I can’t predict the bill.”Whoever owns the bill

“Three people know how we really do this. New hires take six months. If one of them leaves, we lose the standard.”A founder

Where it is being proved

Bleno for construction

Viya Constructions is our design partner for the construction vertical. A signed agreement goes in; a programme, a critical path and a purchase schedule come out. The account says what is built, what is only scoped, and what is still being measured.

Read the account
And close behind it

AetherOps

Bleno can tell you what it did. It can’t tell you what the business actually paid, because it only sees what went through it. AetherOps reads every provider bill you get and joins them under the thing that incurred them, a ledger of closed months, not a monitor.

Learn more

When the person who knows how it is done leaves, the method stays.

Start by naming the three jobs that eat your week. One function, your own material, a ceiling you set. If it does not change what comes back, you will know inside a month.

Visit bleno.io Talk it through