Searce

searce.com

Searce combines advisory services with hands-on technology delivery rather than operating primarily as a research publisher or software vendor. Current practices cover cloud and AI platforms, AI-enabled process redesign, adoption, and security and resilience. Archived catalogs provide additional detail on model implementation, data engineering, application development, and managed operations. Buyers can distinguish assessment and strategy engagements from implementation and operating contracts; archived package names and support terms require confirmation. ^[1] ^[2] ^[3] ^[4] ^[5] ^[6] ^[7]

Services and delivery

AI strategy and enablement advisory

Advisory. Workshops and pilot strategy help customers plan AI adoption. This is an advisory offering, distinct from building or operating models. ^[5]

Machine-learning implementation

Implementation. Builds AI pilots and implements ML models, including hyperparameter tuning. The archived catalog separates implementation from strategy and ongoing model operations. ^[5]

ML operations

Managed services. The archived offering covers integrated ML systems and continuous production operation, with round-the-clock guidance and support. Buyers should confirm current coverage and operational responsibilities. ^[5]

AI workflow and custom-system engineering

Implementation. Maps processes and dependencies, engineers agentic workflows, and rebuilds workflow layers through system consolidation and custom applications. The stated scope sits above existing systems of record rather than automatically replacing core platforms. Delivery includes evaluations, regression testing, and human review. ^[2]

Cloud architecture and modernization assessment

Advisory. Assesses infrastructure and data platforms, identifies modernization opportunities, and reviews architecture, risk, and compliance before migration or platform changes. ^[1]

Cloud migration and platform modernization

Implementation. Implements phased migrations and data-platform modernization, followed by optimization. This is engineering delivery, separate from the preceding assessment and architecture work. ^[1]

Data engineering and business intelligence

Implementation. Migrates databases and builds custom ETL pipelines, warehouses, data lakes, dashboards, and visualizations. The catalog also covers data governance; these offerings do not by themselves establish AI model delivery. ^[6]

Cloud-native application and API development

Implementation. Develops MVPs and cloud-native applications and implements API integrations. The catalog includes an airline application rebuild using the MERN stack and App Engine, alongside Apigee integration services. ^[7]

Security and resilience assessments

Advisory. Assesses security posture, vulnerabilities, compliance, application health, and cloud health, with penetration testing also listed. These assessments are distinct from continuous security operations. ^[4]

Managed security operations

Managed services. Provides continuous SOC coverage, threat hunting, incident response, vulnerability remediation, and SLA-based reporting. The practice identifies Google SecOps and response playbooks as part of its service approach. ^[4]

Workplace AI adoption and workflow implementation

Implementation. Offers workplace AI workflows using Google Workspace, Gemini Enterprise, and Vertex AI, alongside workplace guardrails. Buyers should establish which configuration, custom development, licensing, and support activities are included. ^[3]

Company details

Technology and platforms

Case studies document delivery on both Google Cloud and AWS. Examples include Claude on Vertex AI for document processing, Amazon Bedrock and Kendra for retrieval-augmented generation, SageMaker for predictive ML, and Kafka, Dataflow, BigQuery, and Looker Studio for manufacturing data pipelines. These are engagement-specific examples, not evidence that every platform is included in every service. ^[8] ^[9] ^[10]

The cloud practice names proprietary migration, orchestration, cost-intelligence, and refactoring tools. The reviewed evidence does not establish standalone licensing terms, so these should be treated as delivery tooling rather than separately purchasable products. ^[1]

Engagement and delivery

The AI outcome practice includes an Operate & Rebuild model in which Searce takes operational ownership of a process while rebuilding it. It describes an operating owner, evaluation controls, human review, and an exit path. This differs from a workshop-only engagement or a build handed over immediately after deployment. ^[2]

Track record

For Barti, Searce describes a clinical-fax pipeline with document splitting, identity extraction, patient matching, validation, exception handling, and an audit trail. The customer owns the pipeline in its Google Cloud project. Searce reports 74.6% unattended filing and zero wrong-patient matches across thousands of auto-filed decisions; these are engagement-specific reported results, not general accuracy guarantees. ^[8]

For an unnamed logistics technology customer, Searce reports implementing a synchronized data lake, a Bedrock/Kendra assistant, Amazon Q for QuickSight, and a SageMaker freight-rate prediction pipeline. The prediction error figure is a design target rather than a demonstrated result, and the unnamed customer limits reference verification. ^[9]

The PasaJob case describes migration to Google Cloud, with Searce reporting completion two weeks ahead of schedule and monthly savings of up to 62%. Separately, the Fexco case documents a governed Google Cloud landing zone, BigQuery/Looker delivery pipelines, Azure connectivity, centralized identity policies, and observability integration. ^[11] ^[12]

Scope and limitations

Several detailed catalogs and case studies use archive URLs. Confirm current availability, packaging, support coverage, and commercial terms.
The workplace adoption page does not clearly separate licenses, configuration, custom development, and ongoing support.
Case-study outcomes are Searce’s reported results, not independently verified benchmarks or commitments for future engagements.
Proprietary tooling is documented, but standalone purchasing terms and customer rights are not established.

Questions to ask

Can you provide a comparable production reference and distinguish pilot deliverables from production commitments?
Who owns model evaluations, acceptance criteria, human-review rules, and monitoring after deployment?
Who owns the code, cloud resources, data, and accelerator-dependent components, and what can be transferred at exit?
How are advisory, implementation, cloud and model consumption, licenses, and managed support priced separately?
What SLAs, maintenance responsibilities, incident-response obligations, and handover deliverables will be contractual?

Overview