AI That Gets
Work Done
We implement and manage OpenClaw, the connected agent platform, inside the systems you already run. Your team spends less time on repetitive work and more time on the work that needs them.
Hands-on OpenClaw implementation, systems integration, and Managed AI Operations across industries.
Backed by our 90-Day Money-Back GuaranteeWhat working with it looks like
We don't sell you another AI tool. We build AI into the systems you already run your business on.
We come into your business, learn how the work actually gets done, deploy OpenClaw into the systems you already use, train your team, and stay responsible for keeping it working. Not a strategy deck, and not another subscription handed to your team to figure out.
More gets done
without adding more work.
Before any of the architecture matters, here is what this is actually for. These are the kinds of changes we look for in a business — the reasons the work is worth doing at all.
Give people time back
Repetitive retrieval, preparation, routing, follow-up, reporting, and administrative work gets handled, so your team spends more of the day on judgment and the work only they can do.
Get more from the team you already have
Create capacity before assuming every constraint means another hire. The goal is a team that can take on more, not a smaller one.
Keep work moving after hours
Defined workflows keep monitoring, preparing, routing, and — where you have approved it — responding, even when the office is closed. Anything with real consequences still waits for a person.
Build capacity that compounds
Once your systems, permissions, and context are connected, the next capability is built on a foundation that already exists rather than started from nothing.
We do not promise headcount reductions or a fixed return before we understand your operation. What we look for is capacity: more work getting done, with better information behind it.
Most companies bought AI.
Few of them gained capacity.
The hard part isn't access to AI. It's connecting that capability to the way work actually moves through a business. Big Timber closes that gap by getting inside the operation and building the system.
Where it breaks
Tools, not capacity
Another subscription arrives. The work it was supposed to absorb stays with your team.
Advice that stops at the deck
A roadmap is delivered, nobody implements it, and the operation runs exactly as before.
Automations that don't speak
Nine disconnected scripts, each solving a fragment, none aware of what the others changed.
No one owns it
An API changes, a workflow silently fails, and the capability quietly decays.
What we do instead
We learn the operation first
Economics, workflows, handoffs, bottlenecks, and the informal work nobody documented.
We connect what you already run
Your ERP, CRM, storefront, marketplaces, finance, and messaging become one environment, running on OpenClaw.
We build and we train
OpenClaw workflows and agents ship into production, and your people are taught to work with them.
We stay responsible for it
Monitoring, governance, maintenance, training, and expansion keep the environment useful as the business and technology change.
Why OpenClaw
We evaluated the field: per-seat copilots, point automations, custom agent builds. We deploy OpenClaw because it wins where work crosses systems: one agent platform that reads and acts across your ERP, CRM, storefront, finance, and messaging, with the permissions and audit trail to be trusted with real work. We are opinionated, not captive, and we will tell you when it is not the right fit.
Your systems stop being
separate software.
Each system your company runs holds one part of the truth. We wire them into a single operating environment where a change in one is understood everywhere it matters.
Commerce
- Shopify / Ecommerce
- Amazon & Marketplaces
- Merchandising
- Pricing
Record
- ERP
- CRM
- Accounting / Finance
- Purchasing & Inventory
Signal
- Analytics / Warehouse
- Paid Media
- Email & SMS
- Customer Service
Work
- Slack / Teams
- Project Management
- Code Repositories
- Internal Knowledge
Integration is not a logo wall. It is permissions, data contracts, error handling, and accountability: the unglamorous infrastructure, built on OpenClaw, that makes an AI recommendation trustworthy enough to act on.
How we connect systems →Where capacity
can be built.
Leadership, finance, sales, operations, service, purchasing: capacity can be added almost anywhere work crosses systems. We map the specifics inside your operation, not from a menu.
Capabilities change shape by industry. The same work looks different in ecommerce, a service business, a plant, a distributor, or a professional firm — see how it applies in yours.
Different businesses.
The same implementation problem.
The industry changes. The implementation problem is often familiar: important work is fragmented across people and systems that do not share enough context.
Ecommerce
Inventory, merchandising, marketplaces, marketing, service, supply chain, catalog, finance.
See the detail →Service Businesses
Lead intake, CRM, estimating, scheduling, dispatch, communications, field work, invoicing.
See the detail →Manufacturing
Purchasing, inventory, planning support, quality and maintenance documents, ERP workflows.
See the detail →Distribution & Wholesale
Replenishment, warehouse exceptions, quoting, accounts, orders, logistics, margin visibility.
See the detail →Professional Services
CRM, proposals, project intake, knowledge, documents, reporting, delivery, billing support.
See the detail →Agencies
Client intake, briefs, media planning, creative, analytics, account management, new business.
See the detail →This list is not exhaustive. Big Timber works anywhere meaningful work crosses systems, teams, data, and decisions.
Industries overview →
One business.
Connected intelligence.
Nine isolated automations would each be blind to the others. This is a single system that understands the relationship between them — and knows when a human has to decide.
Isolated automation
Nine tools fire in nine directions. None of them knows what the others just changed.
Connected system
One chain of awareness. Every step below is informed by the step before it.
- 01Marketing
Paid media spend increases
Observed - 02Ecommerce
SKU sales velocity accelerates
Observed - 03Inventory
Coverage falls below supplier lead time
Observed - 04AI Environment
Stockout risk identified
Reasoned - 05AI Environment
Recommended reorder calculated
Reasoned - 06AI Environment
Purchase order prepared
Reasoned - 07Purchasing
Approval request received
Human in the loop - 08Finance
Cash requirement visible
Human in the loop - 09Marketing
Warned about inventory constraint
Human in the loop
One example from ecommerce. The same architecture applies anywhere work crosses systems, teams, and decisions — see AI for ecommerce or how it is built.
Copilot. Coworker.
Operator.
Many companies stop at the first level. The value compounds at the third — but only where autonomy is deliberately bounded. Not every process should run itself, and we will tell you which ones shouldn't.
Copilot
Helps a person work faster. You ask, it answers. All actions belong to a human.
Drafting, retrieval, analysis, and summarization inside the tools your team already opens every morning. The human still drives.
Coworker
Takes assignments and reports back. You hand it work, it prepares and returns a result.
A bounded process runs end to end on a trigger or a schedule, with logging, error handling, and a clear owner when it fails.
Operator
Owns a workflow end to end, running the process inside the authority you set.
The environment monitors the operation, decides what needs to happen, works across systems, executes within defined authority, and escalates exceptions to the right person.
Governance
Autonomy is a setting,
not a promise.
- Defined authority limits per workflow
- Human approval on financial and customer-facing commitments
- Full action logging and audit trail
- Exception routing to a named owner
- Permissions inherited from your existing systems
- Review cadence on output quality
A loop, not
a project.
Each cycle leaves behind working infrastructure and a team that knows how to use it — and reveals the next thing worth building.
Discover
Design
Build
Train
Maintain
Expand
Expand returns to Discover — capability compounds with each pass
What We Do →Managed AI
Operations.
You retain someone to administer your IT. An OpenClaw environment needs the same thing: ownership, monitoring, maintenance, permissions, support, and continuous improvement. Without it, capability decays quietly.
Think of it as an outsourced AI department — an operations partner, not a vendor.
Monitor integrations and workflows
Continuous health checks on every connection and process in the environment.
Troubleshoot failures
When something breaks, it is our responsibility to find it, diagnose it, and get the workflow operating correctly again.
Adapt to change
APIs, models, and software move constantly. We keep the environment current.
Support and train employees
New hires onboard, existing staff level up, and questions have somewhere to go.
Identify new opportunities
We are inside the operation, so we see the next constraint early.
Ongoing AI strategy
Where the capability should go next quarter, and what is not worth doing.
90-Day
Money-Back
Guarantee
Every business is different, so we will not pretend we can promise the same return before we understand yours. What we can do is share the risk.
Big Timber backs its implementation work with a 90-day money-back guarantee. You are not taking the implementation bet alone — if we cannot make the engagement worthwhile, you should not be stuck with the cost.
Subject to engagement terms.
Read Our Guarantee →
Find where
AI fits.
We start by understanding the business — not by pitching a predetermined product. We'll map where AI could create meaningful capacity in your operation, what should stay human, and where it makes sense to start.
- How your operation actually runs today
- Where time, margin, and attention are being consumed
- Which workflows and systems present the strongest opportunities
- What a practical first implementation could look like