What we deliver

What Fortis is engaged to build

Fortis delivers across three connected pillars: database engineering, cloud infrastructure, and AI data engineering, plus the ongoing arrangements for estates that need an engineer on them continuously.

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Pillar 01

Data platform and database engineering

The foundation. Database depth across every major platform, delivering the reliability, performance, and compliance posture that production programs depend on.

Database administration and operations

Full lifecycle DBA work across SQL Server, PostgreSQL, Oracle, MySQL, MariaDB, and MongoDB. Installation, configuration, patching, upgrades, capacity planning, and operational support for systems that cannot go dark.

SQL ServerPostgreSQLOracleMySQLMongoDBMariaDB
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Performance tuning and optimization

Deep analysis using execution plans, wait statistics, and I/O profiling. Query optimization, index redesign, and instance tuning aimed at root causes rather than symptom management.

Execution plansIndex designWait statsBlocking analysisQuery rewrite
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Replication and distributed systems

Availability groups, transactional and merge replication, log shipping, and Kafka event-driven architecture, including systems that operate under intermittent or denied network connectivity.

Always On AGKafkaLog shippingEvent streamingDisconnected sync
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High availability and disaster recovery

Clustering, failover automation, backup chain design, and recovery procedures that have actually been tested. Built to hold uptime commitments and continuity of operations requirements.

ClusteringFailoverBackup chainsDR testingRTO and RPO
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Compliance and security hardening

Database architecture aligned to FedRAMP, FISMA, DoD RMF, and SOX. Role-based access control, encryption at rest, and audit logging designed in from the first commit.

FedRAMPDoD RMFRBACTDEAudit logging
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Troubleshooting and incident response

Root cause analysis for deadlocks, blocking chains, replication failures, corruption, and performance emergencies on production systems, with remediation and post-incident documentation.

DeadlocksBlockingReplication failureCorruptionTier III
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Pillar 02

Cloud infrastructure and modernization

Migration, automation, and cloud-native data platform engineering across all four cloud platforms and their commercial equivalents, with production depth in AWS, inside real compliance boundaries.

Cloud database modernization

End-to-end migration of legacy database estates, primarily into AWS and AWS GovCloud, with Azure Government, OCI and GCP supported where the estate requires it. Assessment, target architecture, execution, validation, and post-migration tuning, with data integrity as the non-negotiable.

AWS GovCloudAzure GovOCIGCPRDS and Aurora
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Data platform engineering and infrastructure as code

Terraform and Ansible for database provisioning, CI/CD pipelines for schema and operational change, automated runbooks, and the compute and network engineering underneath a cloud-native data platform.

TerraformAnsibleEC2Security groupsCI/CDPython
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ETL and data integration

Pipeline engineering across heterogeneous systems at multi-terabyte scale, using SSIS, Informatica, Qlik Replicate, and custom Python, with change data capture, bulk load, and streaming patterns.

SSISInformatica IICSQlik ReplicatedbtCDC
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Cloud architecture advisory

Cross-cloud tradeoff analysis, platform selection, and target architecture design. Grounded in hands-on architecture across all four government clouds, with primary production depth in AWS, rather than vendor documentation.

Four-cloud architecturePlatform selectionCost optimizationRight-sizingFedRAMP
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Pillar 03 Emerging

AI and GenAI data engineering

Production generative AI architecture built on the same patterns as active federal and enterprise data programs, from model integration through the pipelines that feed it.

GenAI platform architecture

End-to-end generative AI platform design on AWS: Bedrock model integration, knowledge base configuration, and direct model API implementation for data-heavy applications.

Amazon BedrockClaude APILLM integrationKnowledge Basesboto3
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Retrieval pipeline engineering

Vector search and retrieval-augmented generation over private datasets, so model responses are grounded in your data instead of invented. Embedding strategy, chunking, and evaluation included.

RAGVector searchOpenSearch ServerlessEmbeddingsS3 data lake
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Agents and autonomous workflows

Design and deployment of agent architectures for multi-step reasoning over operational data, with action groups, tool definitions, and guardrails that keep behavior inside policy.

Bedrock AgentsAction groupsTool useGuardrailsLambda
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Document intelligence pipelines

OCR pipelines that turn physical documents into structured database records, plus image analysis with confidence scoring for inspection and asset workflows.

TextractOCRRekognitionRDS PostgreSQLStructured extraction
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Caching and low-latency data access

Cache-aside architecture with Redis and ElastiCache, key design for access pattern rather than habit, and invalidation strategy that stops stale data from being served confidently.

ElastiCacheRedisCache-asideDynamoDBKey design
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AI delivery pipelines and operations

CodePipeline and CodeDeploy automation for model-backed systems, identity and access architecture, environment separation, and the observability to know when behavior drifts.

CodePipelineCodeDeployGitHubEC2IAM
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Pillar 04

Ongoing and managed engagements

Most of this work does not end. These are the arrangements for an estate that needs an engineer on it continuously, rather than a project that closes. Fortis also supports federal programs and prime contractor delivery teams under the same model, as a scoped engineering workstream rather than staffing. Federal and prime support

Fractional database engineering

A senior database engineer on your estate every month, without the cost or the hiring cycle of a full-time role. Proactive maintenance, performance work, change review, and someone who already knows the environment before something goes wrong.

Monthly retainerNamed engineerProactiveChange review
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On-demand engineering

A block of hours drawn down as needed, for teams that have the capability in house most of the time and need depth occasionally. No monthly commitment, no minimum draw, and the hours do not expire.

Prepaid hoursNo expiryNo minimum draw
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Incident and escalation support

Root cause analysis and remediation when a production database is in trouble: corruption, replication failure, blocking chains, a failed cutover. Arranged in advance so the relationship exists before the incident does.

Root causeCorruptionReplication failurePost-incident report
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Not sure which pillar you need?

Most programs need more than one. Start with a fixed-scope assessment and we will propose the right approach from what it finds.

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