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Intelligence Layer

One account, one ledger, one agent layer. The modules are what you switch on.

Platform overview
The AI Intelligence Layer

One intelligence layer under every module: the same data, the same agents, the same ledger, whichever function you switch on.

  • One layer, every module
  • Shared data and agents
  • Every run on one ledger
Go to The AI Intelligence Layer
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The Architecture

One account carries every brand you run. Modules are switched on per brand, and every run lands in the same ledger.

  • One account, many brands
  • Modules toggle per brand
  • A single billing ledger
Go to The Architecture
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The agent layer

Three kinds of agent. Watchers notice, drafters produce, and Ask Intelligence answers from your own numbers.

  • Watchers run on a schedule
  • Drafters wait for sign-off
  • Answers carry their source
Go to The agent layer
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Security

Isolated tenancy, roles that mean something, and a plain answer about what leaves your account.

  • Per-tenant isolation
  • Roles down to the module
  • Stated data boundaries
Go to Security
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By role

The same product, read by job. For the person who holds the job, and for the company where the platform is the hire.

All roles
CEO / Director

Five dashboards, the ones you describe in a sentence, a weekly review that explains what moved, and a question box that quotes the real figure.

  • A weekly review with causes
  • Dashboards from a sentence
  • Caps on every dollar spent
Go to CEO / Director
Marketing

Brand Content, Social, Search, and Ads on one account, with seven watchers that report what moved and a voice you teach once.

  • 37 text formats, priced runs
  • Six networks, real limits
  • Google ranks and AI answers
Go to Marketing
Sales

A pipeline that won’t let a deal sit without a next step and a date, red dots on the quiet ones, and follow-up mails drafted in your own Gmail.

  • Overdue and quiet deals flagged
  • Drafts wait for your Send
  • Two currencies on every amount
Go to Sales
Project Manager

Kanban and Gantt over the same cards, projects only their team can see, and work that arrives from findings, deals, posts, and validation requests.

  • Five ways a card arrives
  • A team and a planned cost
  • The Friday report as a PDF
Go to Project Manager
Customer Service

An assistant on your website that answers from what you gave it, says so when it doesn’t know, and logs every question it couldn’t answer.

  • On any site, one pasted line
  • Refuses to guess
  • A daily budget per desk
Go to Customer Service
Finance

The pipeline as numbers, every amount in two currencies, and a ledger and caps on every AI run.

  • The pipeline, weighted by stage
  • Two currencies, one rate table
  • Every run on the ledger
Go to Finance
Web

Rank tracking and the keyword gap, page formats that read the page they replace, and a desk on the site that sets no cookie.

  • Ranks in 232 markets
  • Pages written to a format
  • A desk on the site, one line
Go to Web
Human Resources

No HR module, said first. Drafting in the Playground, a handbook employees can question, and onboarding as tasks with owners and dates.

  • A handbook that answers
  • Onboarding as dated tasks
  • Free accounts for every hire
Go to Human Resources

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Services

The people around the product, from the first connection to a named operator on your account.

All services
Onboarding

At least four hours, live: your account set up with you, your team trained, then three months of support.

  • 1,000 USD, 300 back in your wallet
  • At least four hours, live
  • Three months of support
Go to Onboarding
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Data integration

We connect the systems you already run and prepare the data behind them, so every answer has a source.

  • Connectors built and tested
  • Metrics defined once
  • Sources reconciled
Go to Data integration
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Custom features

An agent or a function built for your process, on the same ledger and under the same approval gates.

  • Scoped before it is priced
  • Same gates, same ledger
  • Yours to keep
Go to Custom features
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Dedicated hosting

A single-tenant deployment in the region you choose, with the data boundary written down and an uptime commitment.

  • Single tenant
  • Region of your choice
  • Uptime in writing
Go to Dedicated hosting
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Premium Support

A named contact, committed response times, and a quarterly review of what ran, what it cost and what to change.

  • Named contact
  • Committed response times
  • Quarterly review
Go to Premium Support
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Training

Sessions for the people who will approve, review and ask, so the workforce is used rather than watched.

  • By role, not by feature
  • Live on your account
  • Recorded for the next hire
Go to Training
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Managed operations

Someone reviews what the watchers found, approves the drafts within your caps, and runs the weekly review.

  • Drafts approved in your name
  • Caps respected
  • Weekly review delivered
Go to Managed operations
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About

Why this was built, who is behind it, what it has done for the companies running it, and how to run it well.

About BearingBridge
Why we built it?

What a company can do has been capped by who it could afford to hire. That cap is the thing that moved.

  • Capability, not headcount
  • Written, not benchmarked
  • No lock-in claim
Go to Why we built it?
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Who is behind BearingBridge?

Who built this, what it is independent of, and why that independence is worth stating out loud.

  • Model-independent
  • No data resale
  • Named people behind it
Go to Who is behind BearingBridge?
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How we run the work

The six-phase method every account is built on: a written bearing, a dated baseline, a pilot on real data, and a fix that reconciles the money.

  • A kill criterion, in writing
  • Costs watched as they run
  • An ending without us
Go to How we run the work
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Case studies

Ten builds told the way they ran: the situation, what was built, what changed months later, and what got switched off.

  • Sector and function stated
  • Client-verified figures
  • The stopped work left in
Go to Case studies
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FAQ

The questions that come up before every demo, answered here so the demo can be about your business.

  • Product and billing
  • Security and data
  • Answered in plain words
Go to FAQ
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Testimonials

Customers on the module they actually run, quoted directly, with the module named.

  • Module named each time
  • Quoted, not paraphrased
  • Role and size given
Go to Testimonials
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Changelog

What shipped, dated, newest first. The place to check whether the thing you were promised exists yet.

  • Dated entries
  • Shipped only
  • Linked to the module
Go to Changelog
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Partner program

Run the platform for the companies you advise. Every client is a brand on your account, billed on its own ledger.

  • Clients as brands
  • Separate ledgers
  • Margin on every run
Go to Partner program
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Pricing

Seats are free. You pay for what the AI actually runs, and you see the price first.

Full pricing
How it works

Three sentences, and that is the whole model. No tiers to decode, no per-seat arithmetic to do.

  • No seat licence
  • No annual lock-in
  • Three sentences long
Go to How it works
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Seats

Give an account to everyone who needs one. The number on the invoice does not move when you do.

  • Unlimited accounts
  • Zero per-seat cost
  • Roles still enforced
Go to Seats
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What things cost

Image and video show their price before you run them, everything else its cost the moment it ends, and the machine’s own mistakes are not billed to you.

  • A price on every run
  • Retries are on us
  • Itemized in the ledger
Go to What things cost
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Caps and gates

A daily allowance per person, a monthly cap for the organization and a budget per agent, and an approval gate in front of anything that publishes or spends.

  • Caps per person and agent
  • Approval before publish
  • A zero balance stops the AI
Go to Caps and gates
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Questions

The questions people actually ask about the bill, answered on the page rather than in a call.

  • Overage answered
  • Cancellation answered
  • Migration answered
Go to Questions
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Insights

Three collections, one standard: long-form, sourced, dated, and never behind a form.

All insights
AI Trends

What is moving under the industry: model economics, the Chinese price tier, and how buyers now ask assistants instead of searching.

  • The model market, read closely
  • AI search and citations
  • Every claim sourced and dated
Go to AI Trends
A white leaderboard sheet of six rows on a pale grid, its top row edged and filled in deep cyan, the others in greyModel benchmarksEvery model, scored in publicSourced scores for the text, image and video models in the app, refreshed twice a month. Each figure links to the page it was read on.Compare the models
Best Practices — AI Guide

The working guide: when to buy and when to build, when an agent is the wrong tool, and what survives contact with production.

  • Build-or-buy, decided
  • Architectures that ship
  • Prompts that do real work
Go to Best Practices — AI Guide
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CEO's Opinion

Signed columns from the person running the company: what building agents across six functions actually shows, ahead of the industry line.

  • Signed, never ghostwritten
  • From live builds, not decks
  • Positions, not press releases
Go to CEO's Opinion
A white leaderboard sheet of six rows on a pale grid, its top row edged and filled in deep cyan, the others in greyModel benchmarksEvery model, scored in publicSourced scores for the text, image and video models in the app, refreshed twice a month. Each figure links to the page it was read on.Compare the models
AI model benchmarks

Every model you can pick in the app, for text, image and video, scored on public benchmarks, with the model behind Quick, Balanced and Best.

  • Sortable scores, each one sourced
  • A price level, never an amount
  • No figure ever estimated
Go to AI model benchmarks
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Case studies

Ten builds, told with the failures left in.

Finance, distribution, sales, service, and marketing, each told the way it ran: the situation, what was built, what changed months later, and what got switched off. Sector and function are stated. The client, by agreement, is not.

Builds, told months after handover
10Builds, told months after handover
Business functions, from finance to marketing
5Business functions, from finance to marketing
Pieces of work switched off and left in
4Pieces of work switched off and left in
The index

Start with the one that looks like yours.

Ten engagements delivered by the team behind Intelligence, and they share one shape. That shape is now the platform: where a job on this page has a module that carries it today, the study names it.

These were consulting engagements, priced per phase. The same job as a module is priced per run: money in a wallet that pays for tokens, storage and data, the price on screen before anything spends. The pricing page holds the whole model.

  1. Definitions

    Agreed before anything is extracted.

  2. Clean data

    Before any model sees it.

  3. Written constraints

    Before the design, not after.

  4. Approval gate

    In front of anything that acts.

  5. The log

    Behind everything, with a reason.

Showing 10 of 10

Sector, size, and function stated · client anonymized by agreement · figures client-verified

Operations

Five builds that moved work off the desk.

A dark control panel with one gauge lit cyan and five cables converging into it
ConsultingExecutive reporting

This job runs today on

An executive dashboard the CEO can question.

Five systems, five people, two weeks late. Now one question, answered the same day.

What changed, months later

  • The monthly consolidation exercise disappeared, and nobody has asked for it back.
  • A follow-up question costs nothing. Questions that took a week are answered the same day.
  • Definitional disputes happen once, upstream, in a document with a date and an owner.

The situation

The data existed. It reached the CEO through five people, in five formats, about two weeks late, and shaped on the way. A utilization figure that arrives with an explanation from the person accountable for it is not the same thing as a utilization figure.

Definitions came before extraction, and they were the hardest part: three sessions on what utilization means, when an engagement counts as booked, and what a client is when three subsidiaries buy separately.

What was built

A consolidated store under a semantic layer

Five systems reconciled into one, with entity resolution so a client is the same client everywhere.

Questions in plain language

No query syntax, no report request, no analyst in the loop. Every answer carries its definition, its source system, and its last refresh time.

One metric published as a gap

A requested metric was dropped outright, in writing. The project record’s exact words are quoted with this study.

Where it stopped

Refusing to approximate one metric bought credibility for all the others. It became the decision the CEO cited most often.

“Pipeline conversion by practice cannot be computed from these sources at a quality anyone should act on. Approximating it would produce a number that looks authoritative and is not.”
bearingbridge.ai project record, phase Data
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FinanceGroup finance, multi-entity

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A shorter close, with the ledger untouched.

Coding, matching, and commentary automated. Posting never was, by construction.

What changed, months later

  • The close calendar shortened and held its new length through a year end, the cycle that tests it.
  • Coding queries stopped bouncing. Proposals arrive with their precedent attached.
  • The audit trail improved as a side effect: every coding decision now carries a recorded reason.

The situation

The close ran long every month. Coding queries bounced between shared services and the business units for days, and controllers wrote variance commentary that read like last month’s, because it largely was.

The constraints were written before any design work: no posting without approval, no accrual proposed without evidence, no adjustment to consolidated results, and a full log of every proposal with its reasoning. The external auditors reviewed the design before go-live, and their position was minuted.

What was built

A coding assistant

Proposes account and cost center with its reasoning and the precedent transactions it relied on. Learned from twenty-four months of postings, after the entities’ contradictory habits were fixed rather than automated.

Intercompany matching

Proposes resolutions for breaks instead of only flagging them.

A commentary drafter

First-pass variance explanations from ledger movement plus operational context. Every explanation cites its figures, and the controller’s name goes on the final text.

Where it stopped

Posting to the ledger is not available, by construction rather than by policy. The assistant is wrong often enough to matter on transactions with no close precedent, and its rejections are reviewed quarterly.

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Industrial distributionProcurement

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Supplier documents matched before the truck arrives.

Mismatches used to surface at goods receipt. Now they surface at confirmation.

What changed, months later

  • Most eligible documents process without a person touching them.
  • Exceptions surface at confirmation, while corrective action is still cheap.
  • Supplier reliability became measured data instead of institutional memory.

The situation

Two procurement staff keyed supplier documents by hand: confirmations, shipping notices, and invoices in dozens of layouts, arriving as PDFs and email attachments. Mismatches were discovered at goods receipt, and by then the truck has arrived and the only options left are expensive.

Twelve months of documents from the top forty suppliers were sampled first, and match rules, tolerances and exception criteria were agreed line by line with the people who would live with them.

What was built

Documents now land in an extraction pipeline that turns them into structured records, with a confidence score on every field. A three-way match runs orders against confirmations and receipts inside the agreed tolerances, and anything outside them goes to an exception queue, where each item arrives with a proposed resolution and the evidence behind it.

The same pipeline watches lead times, computed from actual deliveries and not from supplier promises, so drift surfaces before a stockout does.

Where it stopped

Handwritten delivery notes from smaller suppliers were trialed and abandoned: checking the extraction cost more than keying the note. Those suppliers were routed to portal submission instead.

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Industrial equipmentSales operations and finance

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A forecast built to hold more than one future.

Testing a scenario cost a week. Now it is a standing item in the weekly meeting.

What changed, months later

  • Scenario runs went from a handful a quarter to several a week.
  • Accuracy improved against twelve months of spreadsheet forecasts, restated so the comparison is like for like.
  • The argument moved from whose number is right to which assumption is right, which is the argument worth having.

The situation

The forecast was not wrong. It could only hold one version of the future at a time, and testing a second one took a week. Ask what happens if lead times stretch by six weeks, and the honest answer was a week. By then the question had moved on.

Definitions took longer than the model. Pipeline meant different things in different regions, and no one had said so out loud. Five years of orders were reconciled to invoiced revenue, and project business was separated from spare parts and service, because blending them had been hiding the signal in both.

What was built

A statistical baseline

Per-segment time series covering the recurring business.

A driver model for projects

Quotation volume, installed base age, production indices, exchange rates, supplier lead times. Two more drivers were proposed and dropped because they added nothing.

A scenario interface

Plain language in, a range out, with every assumption logged so next month’s meeting replays the run instead of relitigating it from memory.

Where it stopped

Two proposed drivers were dropped because they added nothing the others didn’t already carry.

“The board figure stays owned and signed by a person. The agent produces a range and the assumptions behind it. It does not produce the number that goes to the board.”
bearingbridge.ai project record, phase Bearing
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eCommerceCustomer service

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Support that holds through the peak.

Five question types dominated every season. They stopped waiting for a person.

What changed, months later

  • First response time fell, and the usual seasonal slide didn’t happen.
  • Fewer temporary agents were needed at similar volume.
  • The support team’s day moved to the genuinely difficult cases.

The situation

Response times slipped every peak, precisely when they mattered most, while agents spent the day pasting order numbers into templates. Three months of tickets showed the same pattern in every season: order status, returns, sizing, invoices, and delivery exceptions, all answerable from systems the company already ran.

Before any automation, the policy contradictions got cleaned up. The website said one thing and the macro library said another, so the first job was a single authoritative source both could point to.

What was built

Three tiers of triage

High-confidence, low-risk inquiries are answered and sent. High-confidence but consequential ones queue for one-click approval. Low-confidence ones go to a person with no draft at all, because a bad suggestion is worse than none.

A hard list of never

Payment disputes, damage claims, legal or health matters, and anyone already unhappy. Written down before go-live, not discovered after.

A weekly sample

A fixed share of automated sends is pulled and reviewed every week against the same quality bar, so drift gets caught early.

Nothing in this group here. The filter is set at the index.

Halfway through. The five that follow move from operations to revenue and marketing.

Revenue and marketing

Five builds that moved the number.

Parts sliding together into one document-shaped slab, seams lit cyan
B2B supplySales and pricing

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Quotes assembled from context, not from memory.

When two competitors answer within the day, the supplier who takes days is not in the running.

What changed, months later

  • Turnaround moved from days to the same day.
  • Discount leakage became visible for the first time, because deviations now carry reasons.
  • The client extended the system to spare parts, project quotes, framework agreements, and renewals. None of that was planned.

The situation

The bottleneck was never the arithmetic. It was assembling context: the account, the contract terms, the purchase history, the applicable pricing rules. Hours per quotation, living partly in systems, partly in contracts, and substantially in the heads of three long-tenured people.

The pricing logic had lived in those heads for two decades. Writing it down was the real deliverable: volume breaks, contract terms, freight, currency, lead-time surcharges, all of it. The document outlasts the software built on top of it.

What was built

A quotation now starts wherever the request lands: an email, a portal request, or a call note. The agent pulls the account, its contract terms, and its purchase history together, prices the lines under the documented rules, and hands sales a finished document to check and send.

Cost data refreshes on a fixed cadence, and every deviation from the rules is logged with a reason.

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Industrial exportStrategy

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Market monitoring in two languages.

Competitors published in Chinese. The trade press caught up weeks later. So did the team.

What changed, months later

  • Briefings moved from quarterly to weekly, ahead of the decisions instead of behind them.
  • Chinese-market moves now surface before the trade press, measured by logging both dates on every tracked event.
  • The team’s own measure of success: they stopped being surprised.

The situation

A four-person strategy team tracked markets, competitors, regulation, and tenders by hand, and their findings arrived after the decisions they should have informed. The gap was not breadth. It was language: the Chinese-language sources were effectively invisible, and that was the expensive part.

Signal was defined before anything was collected. Named competitors, technology areas, regulators, and tender portals went on a watch list, with one rule underneath: anything entering a decision paper traces to a named primary source with a date. Aggregators detect. They are never cited.

What was built

An ingestion layer in two languages

Deduplication of syndicated news, and entity resolution across Latin and Chinese names, subsidiaries and joint ventures included.

A weekly brief

Short, with every claim sourced and dated, and a query interface for the questions between briefs.

The original beside the summary

Aligned paragraph by paragraph, so a bilingual colleague checks the reading rather than trusting it. Two summaries proved wrong in month one, on terms where Chinese draws distinctions English collapses.

Where it stopped

Social listening ran for six weeks. Only a handful of items met the signal criteria, and none had been missed by the other sources. It was dropped rather than tuned, because the cost was attention, and attention was the scarce thing.

Five channels of different widths merging into one cyan-lit channel
MediaAnalytics and client reporting

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Campaign numbers that agree on what they mean.

Monthly reporting ate a week, most of it spent defending figures the platforms disputed.

What changed, months later

  • The reporting cycle fell from days to hours.
  • Analysts moved from defending metrics to yield and audience work.
  • Client disputes now end at a dated, signed definition. Rebuilding the report from scratch stopped being the only answer available.

The situation

Every advertising platform counted differently, and clients noticed. Monthly reporting consumed most of a week, and the analysts spent it defending numbers rather than reading them.

The foundation was a metric dictionary with a formal sign-off: which source is authoritative when platforms disagree, how taxonomies map, and how hierarchies, time zones, and currencies resolve. A dated, signed definition ends the conversation. Rebuilding the report from scratch used to be the only answer available.

What was built

Every platform now flows through one normalization layer and comes out in the dictionary’s terms, with history reprocessed so year-on-year comparisons hold. Recurring client reports build themselves with first-pass commentary, and the commercial team gets self-service answers in between.

When sources disagree, the report shows both figures with the explanation. Nobody splits the difference, and three advertisers went on to request that discrepancy view specifically.

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B2B softwareDemand generation

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An ads factory run by two people.

Two marketers, two ad platforms, no time to test. The workspace changed the arithmetic.

What changed, months later

  • Campaign launch collapsed from a week to the same day.
  • Keyword coverage expanded into terms the team had never had time to bid.
  • Budget moved weekly instead of monthly, and cost per qualified opportunity improved against baseline. Two campaigns that looked best on cost per click ranked last on cost per opportunity.

The situation

Paid search and professional-network advertising were a side duty for two people, run across two platform interfaces with separate logins and separate taxonomies. Testing cost time nobody had, so it rarely happened.

The data job that mattered was mapping conversions back to CRM stages. Until that existed, the team could optimize cost per click and nothing more useful. Historical exports were cleaned, landing pages audited, and one naming taxonomy imposed across both platforms.

What was built

The build is one campaign workspace. The team briefs once and gets keyword sets with volume and cost estimates, plus ad variants drafted for each platform. Tests run against agreed thresholds, with budget guardrails set during a calm period, not negotiated mid-campaign, and both platforms connect through their APIs, so the ad managers themselves go untouched on a normal day.

Every reallocation arrives with its reasoning and waits for a click. Approval stayed a human act.

One master panel beside a fanned array of smaller panels in many formats
FMCG foodBrand and trade marketing

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A content factory built on brand context.

Every extra variant used to be a budget conversation. Now it is a marketing one.

What changed, months later

  • Local-market adaptations, the ones that used to get cut, now ship as a matter of course.
  • Cost per asset fell against the prior agency rates, and agency spend moved to concept and photography.
  • The binding constraint is now human review capacity, not budget.

The situation

Each campaign concept produced one set of assets. The retailer formats, audience segments and local-market adaptations were cut, campaign after campaign, because agency retainers were priced per asset, and that turned every variant into a budget conversation instead of a marketing one.

The longest phase came before any build. Six workshops on voice and constraints. Three years of campaign archive collected, photography rights cleared. And a claims register that had gone two years without review, rewritten and signed off by legal before the system generated a single word.

What was built

Context packs per brand

Voice samples, claim constraints, product truth, banned language, and visual rules. The packs drive the quality, not the underlying model.

Format templates per channel

Retailer and platform specifications embedded, so an adaptation is a render rather than a project.

A brief-to-asset pipeline

Copy and still images generated against the packs, with mandatory human approval before release and legal review on every claim.

Where it stopped

Video was scoped, prototyped, and stopped. On-pack accuracy could not be held to brand standard, and music and talent rights were unresolved. The kill was documented, not buried.

Nothing in this group here. The filter is set at the index.

That is all ten. Below, the list most case-study pages leave out.

The standard

How a study gets on this page.

Stated

Sector, size and function. Not the client.

These companies compete on the work described here, so the studies run anonymized by agreement. Everything you need to judge the situation is stated. The one thing missing is the name.

Verified

The figures are theirs.

Every figure is client-verified and published with permission. Where a figure is absent, the outcome is described in operational terms instead, because a number we can’t stand behind doesn’t go up. In a demo, ask for the study closest to your situation and we’ll walk through it.

Complete

The stopped work stays in.

Four of these builds ended with a piece switched off. The kills are part of the record, because a study with the failures edited out is an advertisement.

It is the same standard as the testimonials wall, applied to work instead of words: we would rather publish less than publish something we would have to explain later.

Page reviewed August 24, 2026 · new studies land here as clients approve them

Start

The next study starts the same way.

Pick the job that is costing you the most right now. We start with definitions and written constraints, the same way every study on this page started, and nothing acts without an approval gate in front of it.

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