The company that works between the boxes
The revealing thing about Bits In Glass is that its best sales pitch is a broken handoff. An underwriter waits for a sanctions check. A private-equity employee retypes a deal into three systems. A call-center agent toggles between customer records. Everyone owns a piece of the job, but nobody owns the space between the pieces. That space is where this Edmonton-born consultancy has worked since 2002.
Bits In Glass, which cheerfully shortens its name to BIG, designs, builds and runs enterprise automation. It does not arrive with one proprietary suite and insist that every problem resemble it. Its partner shelf includes Appian and Pega for case management, MuleSoft and Boomi for integration, Creatio for CRM, Databricks and Snowflake for data, AWS for infrastructure, and UiPath and Blue Prism for robotic automation. The client buys those platforms separately. BIG earns consulting, implementation and managed-service revenue by making the collection behave like a system.
That distinction matters. Software vendors sell capability: low-code screens, APIs, AI agents, data lakehouses. Enterprises need an operating result: a claim resolved, a permit issued, a customer onboarded, a suspicious payment investigated. BIG sits between the promise and the process. It maps the rules, connects the records, builds the interface, tests the exceptions and remains available when production develops a personality.
What they actually did
Consider a top-10 global reinsurer. Requests arrived across geographies, roles and authority levels. Underwriters had to coordinate with brokers, cedents and claims people while checking sanctions, internal thresholds and market conditions. The first failure was not a dramatic server crash. It was inconsistency: too many manual routes, too little shared context and no clean way to prioritize incoming business.
BIG built an underwriting workbench on Appian. It created cases, routed requests according to geography and decision authority, cross-checked external databases, and prioritized work using risk tolerance, urgency and user expertise. The company calls the integration layer a “digital spine,” a phrase that earns its keep. The workbench did not replace every system. It gave them a common backbone and a single flow of work. BIG and Appian reported a 40 percent reduction in underwriting processing time and a 30 percent lift in operational efficiency. Appian gave the project its 2024 Europe Insurance Award.
“Bits In Glass has been a critical partner in our Appian development journey.”Melanie Emmel / TPG
At TPG, the starting mess was familiar: spreadsheets, legacy systems and detailed approval rules surrounding a private-equity deal. BIG built a digital deal-entry application, connected it to billing, and automated allocations when preset risk indicators allowed. More than 500 users eventually worked in the Appian environment. When a plug-in refused to integrate, BIG consultants reverse-engineered its Java code to locate the conflict. That small episode tells you more about systems integration than a hundred pictures of clouds. The glossy platform is only half the product; the willingness to open the stubborn box is the rest.
A product catalog made of verbs
BIG’s offerings are easier to understand as verbs than nouns: advise, design, connect, automate, migrate, govern, operate. It sells enterprise AI programs and prototypes; cloud data pipelines and lakehouses; low-code process applications; API-led integration; AI-native CRM; testing automation; and support after launch. Engagements can take the form of individual experts, delivery pods, managed services or build-operate-transfer teams.
The market calls this hyperautomation, intelligent automation or digital transformation depending on the year and the conference badge. The plain version is better: BIG helps large organizations move work through complicated estates of old and new software. Its customers cluster in industries where delay and inconsistency have a price - banking, insurance, healthcare, government, transport, manufacturing, energy and real estate.
The competitors range from internal IT teams to Accenture, Deloitte, Capgemini, Cognizant and Infosys, plus an army of smaller Appian, Pega and MuleSoft specialists. BIG’s position is the “global boutique”: narrower and more personal than the enormous integrators, but broad enough to cover process, integration, data, AI and ongoing operations. It also brings industry accelerators - underwriting, permitting, fraud investigation, regulatory change - so a new project need not begin with an empty canvas.
What it cost - and what the price reveals
BIG does not publish a rate card, and the total costs in its public customer stories are not disclosed. That is normal for customized enterprise work, but it creates an important reading rule: percentage improvements are evidence of direction, not a universal ROI promise. A buyer must add platform licenses, implementation, internal staff time, migration, change management and support, then compare the sum with the cost of delay and error.
One BIG case-study menu advertises an asset-lifecycle project that lowered costs by $100,000 a day. Another describes insurance quoting falling from 48 hours to 10 minutes. These are useful signals because they point to the right denominator. The best automation candidate is not the workflow that looks coolest in a demo. It is the one whose waiting, rework or risk already creates a visible bill.
What changed their mind
The company’s own strategy shifted visibly after Capital Square Partners made a significant investment in February 2024. The amount and valuation remain undisclosed. CSP liked BIG’s platform relationships and its position in hyperautomation; BIG wanted money and expertise for global expansion, new services and acquisitions. In July 2025, the thesis produced its first public deal: BIG acquired U.S. CRM specialist Omnico for an undisclosed price.
Why Omnico? Not because BIG needed another generic capability slide. Omnico brought a seasoned Creatio team and experience in manufacturing, healthcare and financial services. Its founder, Barney Holmes, said the deciding factor was an alignment of values as well as BIG’s technology ecosystem. He joined BIG to lead the CRM practice. A year after taking growth capital, the company had converted financial backing into adjacent expertise and a deeper North American delivery bench.
The technology story changed too. BIG’s current AI pitch is unusually grounded for the category: data first, workflow second, model third. Its AI Primer begins with a practical prototype; its broader programs include governance, pipelines, agents, deployment and maintenance. This is less exciting than dropping a chatbot on every screen, which is precisely why it is credible. An agent that cannot access trusted data or act inside a governed process is a theater prop with an API bill.
The playbook you can copy
Steal the sequence, not the software stack
- Name one costly queue in minutes, dollars, error rates or regulatory exposure.
- Draw the real route, including approvals, authority limits, data sources and ugly exceptions.
- Choose a thin first slice that reaches production and can be measured.
- Connect systems through a stable integration layer instead of rebuilding everything at once.
- Put early versions in front of users, then fund the next workflow with the first result.
The Guided Care implementation, delivered by Omnico before it joined BIG, is a neat example. The organization replaced aging systems with Creatio and went live in four and a half months. Users saw prototypes early and shaped them while the team worked. By launch, the project included more than 300 enhancements beyond the original plan. The transferable idea is not “add 300 things.” It is to shorten the distance between a user noticing a problem and the delivery team changing the workflow.
When the playbook does not work
Automation is leverage. It amplifies clarity, but it also amplifies confusion. The approach breaks when an organization has no accountable process owner, cannot agree on decision rights, or treats every exception as sacred. It also breaks when source data is unreliable, security and governance arrive late, or employees meet the new system on launch day.
A workflow crossing five departments still needs one person who can decide what “done” means.
AI and automation move faster, but neither can infer a trustworthy customer record from duplicate chaos.
If every edge case is untouchable, the build becomes a museum of organizational compromise.
A clever prototype is not a business case unless the measured benefit survives licenses, support and change.
There is another condition: a boutique only works if the client values judgment more than raw headcount. A giant, standardized global rollout may favor a larger integrator. A tiny company with clean modern tools may not need an integrator at all. BIG fits the difficult middle and upper end - organizations large enough to have legacy complexity, regulated enough to need controls, and motivated enough to let an outside team redraw the route.
That is the quiet appeal of Bits In Glass. Its story is not that software can transform a company. Everyone has heard that one. Its story is that transformation happens in the specific, occasionally annoying work of deciding who acts, which data counts, what happens next and how the old machine speaks to the new one. The bits between the boxes are boring. They are also where the value leaks out.