Every service follows the same structure: problem, what TFIT does, approach, deliverables, engagement, request. Pricing depends on scope. Request an assessment.
Problem: you want AI for contracts, support and knowledge search, without sending sensitive data to a public cloud where you lose track of residency and training reuse.
Problem: lock-in, licensing surprises and redesign every time a vendor changes terms. You need portability between datacenter and cloud.
Problem: snowflake servers and untracked images. You need a standard container unit: UBI-based, scanned, supportable in production.
Problem: defects found in production. TFIT moves quality left: SonarQube plus AI-assisted review before merge, reuse before buy before build.
Problem: log-only monitoring tells you after the incident. TFIT builds metrics-first, predictive triggers per stack: JVM, DB, filesystem, pools.
Neither. TFIT places each workload where residency, cost and operability fit best, documented in an ADR.
Advisory or assessment first: a session or fixed-scope review with a prioritised roadmap. No big transformation required.
Estimate vs proposal vs quote vs statement of work vs invoice. Each is labelled explicitly.