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Enterprise AI Deployment
Production AI inside core operations
You have seen the pattern. A model that demos well, a steering committee that approves phase two, then eighteen months of integration meetings and a quiet write-off.
Sigmoidal has been deploying enterprise AI systems into production since 2016, years before the LLM wave. Our systems run inside the ERP, the research desk, and the dispatch queue, with SLAs, named on-call engineers, and continuous model monitoring. They are judged by the number that reaches the board deck, not the demo that reaches the steering committee.
Capabilities
What we build and run in production
Five capability areas, one standard: nothing ships without an SLA, an owner on call, and a metric the CFO recognizes.
AI document processing and workflow automation at scale
Extraction and routing for filings, earnings transcripts, broker research, and data-room exports. Confidence thresholds decide what flows straight through and what lands on a human desk; data contracts, throughput SLAs, and exception queues come standard. At one global investment manager, the pipeline structured roughly 400,000 documents in its first year.
AI decision support and analytics systems
Deal-flow scoring, instrument-failure prediction, estimate-revision screens before the market opens: a defensible number in front of the decision-maker before the meeting starts. One system, trained on a private equity firm's 32,000 historical deals, compares each new target against what the firm bid, what it passed on, and how those calls aged. Drift and calibration monitoring keeps the number trustworthy in month eighteen; every output traces to source data for the day the audit committee asks.
Applied LLM systems with governance
Retrieval, summarization, and drafting on foundation models, with the controls a regulated enterprise requires: evaluation suites before every release, full prompt and output logging, role-based access, human review gates where risk warrants. At the investment manager, drafting is citation-first, every sentence linked to its source, and 70% of routine earnings summaries start from an automated draft. No LLM output reaches a customer or a regulator without a documented control path.
Legacy system integration and MLOps
An AI system is only as good as its connection to the ERP, the LIMS, or the twenty-year-old data warehouse it must read from. At one private equity firm, that layer reads portfolio-company ledgers across mismatched ERPs and charts of accounts: 89% of portfolio financial consolidation automated, every figure traceable to its source ledger line. We build the integration layer, feature pipelines, and model CI/CD: automated retraining, canary deployments, rollback in minutes. On-call is written into the contract, with committed response times.
Security and compliance posture
We work inside your VPC or on premises where data residency demands it, and document model risk to a standard your compliance function can put in front of an examiner. At a global pharmaceutical leader, the telemetry pipeline was qualified as a data source under the client's quality system; model changes run through the client's own change control. At the investment manager, access controls map to the firm's information barriers. Change management is part of delivery: training, runbooks, sign-off from the people who own the process today.
Method
The same four steps since 2016
The models and the tooling have turned over many times since the early predictive-analytics work for organizations including Novartis. The sequence has held because it is judged on the metric it was hired to move.
Understand the business problem
A two-week diagnostic with the people who own the P&L and the process. We leave with a one-page definition of the decision the system will improve, the metric it will be judged by, and the baseline it must beat. If we cannot write that page, we do not proceed.
Build the system
A small senior team, working software from the first sprint, weekly demos against your real data under your access controls. Your platform and security teams join architecture reviews from day one, because their sign-off is a launch dependency, not a formality.
Deploy to production
Canary releases, load testing against peak volumes, rollback plans, runbooks for your operations team, and a change-management plan for the people whose work changes. A working system is in front of your users by week six. At a global investment manager, the first production release shipped in week seven.
Measure the outcome
We instrument the business metric alongside the model metric and review both quarterly with your executive sponsor. At the pharmaceutical leader, the service organization audited every model prediction against the actual outcome for a full quarter before anyone was asked to rely on the system. Engagements are structured so that our success and yours are the same number.
Proof
Outcomes we can defend in a reference call
One engagement, end to end: every figure below comes from the same production deployment at a global investment manager, measured by the client's own operations team. The pharma, private-equity, and consolidation results above carry their own case studies. References are available on request.
- ~400,000
- documents structured by the pipeline in its first year, every one traceable to its source Case study
Client story
40% research productivity gain, no added headcount
A global investment manager with more than $80B under management came to us with equity research analysts spending close to 60% of their week assembling information: pulling filings, cleaning transcripts, reconciling broker estimates. An earlier internal pilot, a general-purpose chatbot, had failed compliance review because it could not show where its answers came from.
We built a citation-first research platform inside the client's own cloud: document processing across filings, transcripts, and broker research, standing market-analysis jobs delivered before the open, and drafts in which every sentence links to its source. Twelve months after the first production release, the firm's own operations team measured a 40% research productivity gain. The platform serves 200+ daily users across the research and investment teams, and the reclaimed hours went into 24% more names under coverage with no added headcount.
Engagement model
Embedded teams that own the outcome
A Sigmoidal team arrives senior and stays small: an engagement lead who has shipped production AI before, three to five senior engineers across ML, data, and platform, and a domain specialist who speaks your industry's language, whether that is GxP, MNPI, or OEE. The team embeds in your organization, works in your environment, and reports against your metric.
We deliver a running system and stay accountable for the number it was built to move. At the pharmaceutical leader, our team ran the system jointly with the client's service organization and then transferred operations; the client owns it today. At the investment manager, our engineers carried the on-call rotation after launch, then handed it to the client's platform team with full runbooks. Median engineering tenure is over seven years, and there is no pyramid of juniors behind the people you meet.
Bring us the pilot that stalled
The first working session is with a partner who has built these systems and a principal engineer. Within two weeks you will have a written assessment: whether the system is worth building, what it will cost, and what it should return. If the answer is no, we will say so.