Database engineering for the data you depend on.

Find what's slow, what's risky, what's missing.

We inventory your databases, validate that backups actually restore, audit who has access to what, and benchmark performance against your real workload. Output: a written report ranking the top fixes by ROI, in plain English. No commitment beyond the check. Engine, cloud, and tooling matched to your stack — ask in scoping.

What you get

• Written report with prioritized fix list
• Verified test restore of at least one production database
• Access / permission inventory
• Performance baseline (top wait stats, slow queries, missing indexes)

Engagement

Three commitment sizes scaled to your estate. Lite: $1,500 — 1 week, single database, plain-English summary, one free fix. Standard: 2 weeks, full instance, formal risk register. Premium: 3-4 weeks, full estate audit and executive readout. Standard and Premium are quoted after a 30-minute scoping call.

Make slow databases fast — measured, not guessed.

We baseline your real workload from Query Store and wait statistics, reproduce the worst offenders in a non-prod harness, and diagnose blocking, plan regressions, parameter sniffing, missing or misaligned indexes. Apply changes, measure before-and-after against the same baseline, and leave behind a runbook your team can maintain — real numbers, measured.

What you get

• Workload baseline (Query Store, wait statistics) + non-prod reproduction harness
• Specific index / query / configuration changes, applied and reviewed
• Before/after metrics against the same baseline
• Maintainable runbook handed to your team

Engagement

2-4 week fixed-fee project. Final fee agreed in writing after a 30-minute scoping call.

Move databases off aging servers, or between platforms — without downtime surprises.

Two flavors, both done with object-by-object validation and staged approval gates. (1) On-prem → cloud, sized correctly, monitored from day one, documented rollback path. Near-zero-downtime cutovers when the workload demands it, planned around your downtime tolerance and proven on prior migrations. (2) Cross-platform: stored procedures, functions, views, and ETL logic translated for semantic preservation, not literal syntax. Test harness compares output row-by-row before sign-off. Infrastructure provisioned as code so the new environment is reproducible from day one. Cloud-agnostic.

What you get

• Migration plan with explicit rollback procedure
• Staged cutover with validation gates between phases
• Object-level test harness (procs, functions, views, ETL)
• Performance and cost baseline on the new platform
• Operator runbook for ongoing maintenance

Engagement

Project-based. Scope and timeline depend on database size, object count, and downtime tolerance. Final fee agreed in writing after a 30-minute scoping call.

Safely point Claude, Copilot, or your own agents at your business data.

Set up Model Context Protocol (MCP) servers against your existing databases — read-only by default, dedicated AI-purpose login, least-privilege schema access (RBAC), and audit logging. Sequenced staging-only first, then promoted to production after security review. Designed to hold up under SOC 2, PCI DSS, and SOX audit evidence requests. Engine and cloud matched to your stack.

What you get

• MCP server configured against your databases
• Read-only AI service account with least-privilege schema scoping
• Audit-logging plumbing so you can see exactly what your agents read
• Documented allow-list of tables / views the AI can reach
• Optional: reporting API + dashboard integration

Engagement

2-4 week project covering one staging environment plus production cutover. Final fee agreed in writing after a 30-minute scoping call.

A fixed-scope engagement for testing data and AI product ideas before committing to a full build.

The Opportunity Validation Sprint helps organizations determine whether a proposed data product, AI workflow, signal engine, internal tool, or automation concept is commercially and technically viable. Instead of beginning with software development, the engagement tests the idea's most important assumptions against real data sources, operating constraints, vendor alternatives, implementation costs, and measurable business value.

What we do

• Define the decision the proposed system must improve and the evidence required to justify investment.
• Identify the assumptions that would cause the project to fail if they are wrong.
• Test data availability, timeliness, completeness, ownership, and acquisition cost.
• Evaluate whether existing commercial products already provide the required capability.
• Run targeted research, source validation, sample extraction, or retrospective backtesting.
• Assess where AI, automation, and human review are appropriate — and where they introduce unacceptable risk.
• Design a gated implementation plan that funds the next stage only when the evidence supports it.

What you get

• Executive viability assessment
• Assumption and risk register
• Data-source and vendor analysis
• Technical feasibility review
• Sample workflow or backtest results
• Recommended buy, build, partner, narrow, or stop decision
• Phased implementation roadmap
• Preliminary architecture, budget range, and validation gates

Best for

• AI product concepts
• Data-driven lead or opportunity detection
• Internal workflow automation
• Reporting and decision-support systems
• Commercial data products
• Projects dependent on external or incomplete data
• Initiatives where a failed build would consume significant time or capital

Engagement outcome

The engagement ends with a defensible decision — not a speculative prototype. The result may be a validated build plan, a narrower and more commercially viable product, a recommendation to purchase an existing solution, or evidence that the original concept should not be funded.

Engagement

2-4 week fixed-scope assessment. Build proceeds only after the validation gates pass. Final fee agreed in writing after a 30-minute scoping call.

A managed automation system for producing and distributing consistent, brand-aligned content.

The AI Content Operations Platform gives lean teams a repeatable way to plan, produce, review, and distribute content without adding a full internal production staff. The engagement replaces fragmented manual work with a governed content pipeline that combines AI-assisted production, human approval, automated publishing, and reusable market-specific configuration.

What we build

• A scheduled content-production workflow for scripts, captions, graphics, and short-form video
• Brand, audience, market, and campaign rules maintained as configuration rather than hard-coded logic
• Human review and approval gates before publication
• Automated distribution across supported social and content platforms
• Content calendars, publishing schedules, and reusable campaign templates
• Infrastructure-as-code, deployment controls, and change validation
• Monitoring, failure alerts, retry handling, and operating documentation
• Performance reporting to identify which topics and formats should be repeated or retired

What you get

• Content operations assessment
• Platform and workflow architecture
• Brand and approval controls
• Automated production pipeline
• Distribution integrations
• Campaign and market configuration
• Monitoring and exception handling
• Operating runbook
• Team training and handoff
• Optional ongoing managed operation

Best for

• Professional-services firms
• Real-estate teams and advisors
• Multi-location businesses
• Founder-led companies
• Organizations with limited marketing staff
• Teams that need consistent content across multiple markets or brands
• Businesses spending too much time on repetitive content production and publishing

Engagement options

Platform Setup

Design and implement the production, review, and distribution system.

Managed Content Operations

Operate the approved workflow on an ongoing schedule, including production oversight, publishing, monitoring, and monthly refinement.

Market Expansion

Extend the platform to new regions, audiences, campaigns, or business units through configuration rather than rebuilding the system.

Engagement outcome

The engagement delivers a controlled content operation rather than a collection of disconnected AI tools. The system reduces repetitive production work, improves publishing consistency, preserves human oversight, and makes it practical to expand content across markets, campaigns, and channels without rebuilding the workflow each time.

Engagement

Platform setup is a fixed-scope project; managed operations and market expansion are scoped as ongoing options. Final fee agreed in writing after a 30-minute scoping call.

A senior DBA on call, without a full-time hire.

Monthly retainer for businesses where database downtime costs money. Predictable monthly fee. Same-day response on incidents during business hours. Monthly health check report. Quarterly business review where we surface the next bottleneck before it becomes an incident. Pairs with any of the engagements above as the support tier afterwards.

What you get

• Same-day incident response (business hours)
• Monthly health-check report
• Index / partition / archive maintenance on schedule
• Quarterly review with the ranked next-fix list
• Direct line to the engineer doing the work, not a ticket queue

Engagement

Three retainer sizes scaled to your hours-per-month need: Starter (8 hrs), Growth (16 hrs), Scale (32+ hrs). Monthly fee quoted after a 30-minute scoping call. Pairs with any project engagement after delivery.