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

80%
of AI projects fail.
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.

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
In practiceA 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
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
In practiceA launch goes live in every market on the same morning, with the local copy, visuals, and pages drafted from the brand library.
Plugged into
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
In practiceA 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
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
In practiceA 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
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
In practiceA 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
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
In practiceThe same supplier invoice posted in two entities gets flagged the day it lands, not during an audit two years later.
Plugged into
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
In practiceA supplier starts shipping late in ways nobody has escalated yet, and planning reroutes the order before the plant notices anything missing.
Plugged into
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
In practiceSeveral 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
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
In practiceMonday status writes itself from tickets, calendars, and commits, and the meeting is spent on the one workstream that actually slipped.
Plugged into
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
In practiceA 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
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
In practiceA 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
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
In practiceA 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
You get
Fewer tickets, faster fixes
Four pillars, one value chain.
Data is the raw material. AI puts intelligence inside it. Tech turns that intelligence into products your team runs without us. Consulting is the practice that carries you through all three.
An honest audit of what you have, foundations sized to your use cases, and governance that holds up across jurisdictions.
Get your data readyUse cases that pay back, prototyped on your data with a cost per run, benchmarked across Western and Chinese models.
Find the use cases that matterCustom builds, integrated into the systems your team already opens every morning. We run our own product on this stack.
From pilot to productionThe practice, end to end
Strategy, pilots, and adoption on dated baselines and metrics agreed in writing, including the recommendation to stop when the evidence says stop.
How we workThe 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.
Marketing
[ ready today ]Content from roadmap to live page, with the company knowledge built in.
Sales
[ ready today ]Campaigns that know what has run and which features to push.
Project Management
[ ready today ]Delivery status assembled from the tools your teams already use.
Your function
[ tailored to you ]Same foundation, plugged into your context, your data, and your tools.

[ 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.
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- Bearing000°Deciding where AI is worth pointing, then committing it to paper.
- Data060°Finding the data, cleaning it, and moving it to where the work happens.
- Build120°Whatever the use case calls for gets built, on your data.
- Run180°A small, timeboxed pilot on real data with real users.
- Fix240°The same measurement as the baseline, same method, new date.
- Scale300°Positive return proven, the pilot graduates to production.
What we publish.
Articles that hold themselves to the same standard as our engagements: tested before publication, sources linked, prompts included in full. When we stop something, we write that up too.
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.
Prefer email? hello@bearingbridge.com


