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[ SYS // BEARINGBRIDGE ]LIVE

We put intelligence inside your data.Then we build the product around it.

BearingBridge is an AI consultancy with one operating rule: evidence over hype. We measure before we recommend and agree on the metric before we build. When the evidence says stop, we say stop.

Operating rule
Evidence over hype
Metric
Agreed before we build
Stop
When the evidence says stop
Two colleagues stand at a desk in a late-afternoon office, reading a running agent console on a monitor together
// baseline · metric · kill criterionAgreed in writing, before anyone builds.
[ Why we exist ]

80%

of AI projects fail.

abandoned or never scaledproven with evidence

RAND Corporation,The Root Causes of Failure for Artificial Intelligence Projects, 2024

Most AI projects fail politely.

The pilot demos well and the steering committee applauds. Then the numbers never move, nobody wrote down what success meant, and the budget quietly migrates to next year's pilot.

In our experience the cause is rarely the technology. It is the absence of a baseline, a metric, and the discipline to read them honestly. We built a practice, a method, and eventually our own product against exactly that failure mode.

Cyril Drouin presents a performance chart to a steering committee that applauds at the end of a pilot demo
// field reportThe pilot demos well. Then the numbers never move.
[ Who it is for ]

Intelligence on top of your data.

Data was called the new oil. Intelligence is the refined product, and AI is the refinery. We plug into the data your organization already produces and turn it into analysis, content, and decisions your teams can act on the same day.

Pick a chair. These are examples from real life, and the list keeps growing.

// ceo

The CEO

Understands, analyses, and forecasts the numbers at their fingertips, without asking several teams to report and merge data first.

  • Ask one question, get the consolidated answer across every entity
  • Scenario forecasts weighted by market signals, not one static budget
  • A morning brief that reads every report so you do not have to

A group CEO asks why margin slipped in one country last month and has the answer, pulled from every subsidiary, before the board call instead of after it.

Plugged into

ERPCRMBI exports

You get

Forecasts on demand

// marketing

The marketing team

Creates content at scale with the full company knowledge behind every piece, instead of starting from a blank page.

  • Hundreds of copy and visual variants, tested instead of debated
  • Campaigns carried into every market language, brand voice intact
  • A goal tracker that flags a campaign the day it drifts

A launch goes live in every market on the same morning, with the local copy, visuals, and pages drafted from the brand library.

Plugged into

Brand assetsProduct docsWeb analytics

You get

On-brand content, shipped

// sales

The sales team

Runs ad campaigns at scale, knowing what has already run and which product features to push, and tests hundreds of formats and visuals.

  • Leads qualified and prioritized before the first call
  • CRM hygiene and follow-ups handled by an agent
  • Forecasts built from the pipeline as it moves

A rep opens Monday with the accounts most likely to reorder already at the top of the list, each one carrying the reason and the last conversation.

Plugged into

CRMAd accountsCreative library

You get

Campaigns that learn

// brand

The brand leader

Monitors the competition in real time through market-monitoring agents, instead of waiting for the quarterly report.

  • Competitor price and message changes flagged the day they happen
  • Visibility tracked across search, social, and AI assistants
  • Review streams distilled into one weekly signal

A competitor quietly cuts its entry price on a marketplace, and the brand team reads it that morning rather than finding it in the quarterly review.

Plugged into

Competitor sitesSocialMarketplaces

You get

Alerts, not reports

// support

The support team

Lets an intelligent assistant answer the common client questions on the spot, and brings in people only where they are truly needed.

  • Common questions answered instantly, in every language
  • Tickets triaged and routed before anyone opens them
  • People pulled in only on the cases that need judgment

A client writes in at midnight, in Spanish, about a late delivery. The assistant answers from the order record, and pulls in a person only because a refund is involved.

Plugged into

HelpdeskKnowledge baseProduct docs

You get

Answers in seconds

// finance

The finance team

Closes the month with figures pulled from every system, variance analysis drafted, and anomalies flagged before they become surprises.

  • Anomalies flagged across millions of transactions as they post
  • Invoices matched and processed without rekeying
  • Probability-weighted scenarios instead of one static forecast

The same supplier invoice posted in two entities gets flagged the day it lands, not during an audit two years later.

Plugged into

ERPInvoicesBank feeds

You get

A close without surprises

// operations

The operations team

Forecasts demand from order history and market signals, and anticipates stockouts and supplier risk instead of reacting to them.

  • Demand predicted from orders, seasonality, and market signals
  • Inventory balanced across warehouses before stockouts hit
  • Supplier delays spotted upstream, not at the dock

A supplier starts shipping late in ways nobody has escalated yet, and planning reroutes the order before the plant notices anything missing.

Plugged into

OrdersInventorySupplier data

You get

Risk seen early

// procurement

The procurement team

Consolidates vendors, monitors supplier performance, and prepares negotiations with the full spend picture on the table.

  • Vendor overlap found and consolidated
  • Supplier onboarding and compliance checks run by agents
  • Routine negotiations prepared with the full price history

Several business units turn out to buy the same packaging from different vendors at different prices, and the next negotiation opens with all of it on one page.

Plugged into

Supplier dataContractsSpend data

You get

Spend under control

// projects

The project manager

Gets status assembled automatically from the tools teams already use, with slipping deadlines surfaced before they escalate.

  • Status assembled from tickets, calendars, and commits
  • Slipping deadlines flagged before the review meeting
  • Meeting notes turned into tracked action items

Monday status writes itself from tickets, calendars, and commits, and the meeting is spent on the one workstream that actually slipped.

Plugged into

TicketsCalendarsMeeting notes

You get

Status without meetings

// legal

The legal team

Reviews contracts against your own playbook and tracks the regulations that touch them, at reading speed.

  • Contracts reviewed with the risky clauses flagged
  • Regulatory changes mapped to the policies they touch
  • First drafts from your own precedent, not a blank page

A framework agreement comes back from a client with the liability clause quietly rewritten, and the review catches it against your own playbook the same afternoon.

Plugged into

ContractsRegulationsPrecedents

You get

Risk seen before signing

// hr

The HR team

Gives every employee an assistant that knows the policies and screens applications against the actual job, freeing time for the human part of the work.

  • Applications screened against the actual job, not keywords
  • Onboarding tailored to the role and the person
  • Policy questions answered instantly for every employee

A shortlist for a technical role is built from what the candidates have actually done, and the recruiter spends the day interviewing instead of reading the inbox.

Plugged into

PoliciesApplicationsOrg chart

You get

Time for the human part

// it

The IT team

Correlates incidents across systems, answers internal requests from your own documentation, and keeps legacy systems legible.

  • Incidents correlated across logs before users notice
  • Internal helpdesk answers drawn from your own docs
  • Legacy code documented and explained on demand

A checkout error is traced across the payment gateway, the API, and the database while the on-call engineer is still reading the alert.

Plugged into

LogsTicketsInternal docs

You get

Fewer tickets, faster fixes

[ Proof ]

The first step: see AI at work.

The fastest way to understand what AI can do for your organization is not a slide deck. It is a working platform. bearingbridge intelligence is that first step: AI modules and agents we have already developed, running on real tasks, with every cost visible on screen as it runs.

Each module runs as it is, or gets tailored to your context, your data, and your tools. Modules are ready today for Marketing, Sales, and Project Management, and the same foundation carries to the next function you name.

bearingbridge intelligencemodules

Marketing

[ ready today ]

Content from roadmap to live page, with the company knowledge built in.

Content at scaleBrand knowledgeEvery market and language

Sales

[ ready today ]

Campaigns that know what has run and which features to push.

Hundreds of ad variantsFeature-level targetingSpend visible live

Project Management

[ ready today ]

Delivery status assembled from the tools your teams already use.

Auto status reportsRisk flagsTool integrations

Your function

[ tailored to you ]

Same foundation, plugged into your context, your data, and your tools.

The bearingbridge intelligence sign-in screen, live at intelligence.bearingbridge.com
// the platform, as it runs today

[ Both sides of the wall ]

We work both AI ecosystems, not one.

Most consultancies know the Western stack. Few know the Chinese one. The models on each side of the Great Wall are built, priced, and governed differently, and the right choice depends on your data and your markets.

Westoutside China

  • OpenAIGPT
  • AnthropicClaude
  • GoogleGemini
  • MetaLlama
  • Mistral AIMistral
  • xAIGrok

Eastinside China

  • QwenAlibaba
  • DeepSeekDeepSeek
  • ERNIEBaidu
  • GLMZhipu
  • KimiMoonshot
  • DoubaoByteDance

We have worked on both. So we recommend a model on the merits, not on which side we happen to know. We benchmark GPT, Claude, and Gemini against Qwen, DeepSeek, and their peers on your task, then advise on which to choose, how to use it, and how to run it in production, data residency included.

One provider is a default.Two is a decision you can defend.

[ Method ]

First a bearing, then proof.

An azimuth is the angle between north and where you are heading. Navigators take one before they move and check it as they go, because feeling on course and being on course are different facts.

Our six legs run the same way. We decide where AI is worth pointing and write down the result that means we stop. Then the data, the build, a pilot with real users, and the same measurement again on a new date. Scale is the leg the other five are protecting.

Walk the six legs
000°Bearing// heading held, then checked
  1. Bearing000°Deciding where AI is worth pointing, then committing it to paper.
  2. Data060°Finding the data, cleaning it, and moving it to where the work happens.
  3. Build120°Whatever the use case calls for gets built, on your data.
  4. Run180°A small, timeboxed pilot on real data with real users.
  5. Fix240°The same measurement as the baseline, same method, new date.
  6. Scale300°Positive return proven, the pilot graduates to production.

Talk to us

Talk to us about AI.

A conversation with the senior team about your markets, your data, and where AI would actually pay back for you. No slides, no obligation, and if the honest answer is that AI is not your next move, you will hear that too.