Enterprise Software Development Company in San Francisco

San Francisco carries the highest engineering rates in North America, so a realistic budget conversation starts with what you are buying rather than a rate card. An architecture assessment covering system inventory, domain decomposition, data classification, a migration sequence with a routing seam design, and a costed roadmap typically runs USD 40,000 to 95,000 over six to ten weeks. A production platform or a single core service replacement, shipped end to end with identity, authorization, audit logging, accessibility conformance, and observability, typically runs USD 200,000 to 600,000. A multi-year enterprise program covering core system replacement plus an internal developer platform, a portal, an identity layer, and a governed data platform runs USD 600,000 to 2,500,000 and up, and on those the variable that moves the number most is the number of integrations, not the number of screens. Codazz delivers from Edmonton and Chandigarh rather than a San Francisco office, which puts our blended rate well below Bay Area consultancy pricing at equivalent seniority. That is a real trade and we state it plainly: you get Pacific-hours overlap and an overnight build window, not someone who can attend a hallway conversation. We quote fixed fee against a signed statement of work.

SB 53, the Transparency in Frontier Artificial Intelligence Act, is Chapter 138 and was approved by the Governor on September 29, 2025. It adds Chapter 25.1 to Division 8 of the Business and Professions Code, Section 11546.8 to the Government Code, and Chapter 5.1 to Part 3 of Division 2 of the Labor Code. The obligations fall on large frontier developers rather than on you as a downstream enterprise: publishing a frontier AI framework, publishing transparency reports, reporting critical safety incidents to the California Office of Emergency Services, and whistleblower protections for covered employees. The statute also directs a CalCompute framework report on or before January 1, 2027. Because OpenAI and Anthropic are San Francisco companies, the practical effect for an enterprise buyer here is that meaningful published artifacts now exist to put into your vendor diligence file rather than a marketing page. Separately, if you train or fine-tune your own generative system and make it available to Californians, AB 2013, Chapter 817, approved September 28, 2024, requires training data documentation on your site, with obligations beginning January 1, 2026. And SB 942 as amended by AB 853, Chapter 674, approved October 13, 2025, moves generative AI provider disclosure duties to August 2, 2026 and adds large online platform provenance duties from January 1, 2027.

We do not do replace-in-place rewrites, because the failure mode is well known: a parallel build that has to reach full feature parity before it can carry any traffic, and it never does. We use a strangler-fig pattern instead. A routing seam goes in front of the legacy system first, usually a reverse proxy plus an anti-corruption layer that translates the legacy contract into a clean domain contract. Then one bounded context moves at a time, chosen by a combination of business value and coupling, and traffic shifts behind a flag so rollback is a route change rather than a restore. Data usually moves last for a given context, with change data capture keeping the legacy store in sync during the transition so both systems can read consistently. Every context that moves gets its own tests, its own on-call story, and its own observability before it takes production traffic. This is slower to describe and much faster to realise value, because something is live within a quarter and the program keeps earning its budget instead of asking for another year. It also means an unexpected budget freeze leaves you with a working hybrid rather than a half-finished replacement and a legacy system nobody maintained.

The Department of Justice issued its ADA Title II web and mobile accessibility final rule on April 24, 2024, adopting WCAG 2.1 Level AA as the technical standard for web content and mobile applications provided by state and local governments. Compliance dates split on population: entities serving 50,000 or more people must comply by April 26, 2027, and entities under 50,000 plus special districts by April 26, 2028. The City and County of San Francisco is far above the threshold, so every department platform, including SFMTA, the Port, San Francisco International Airport, and the Department of Public Health, is on the April 26, 2027 clock, and so is anything a vendor builds for them. That deadline is close enough that retrofitting is now the expensive path. We build conformance in from the first sprint: semantic markup and correct heading structure, keyboard operability on every interactive element, visible focus, colour contrast checked in design tokens rather than at review, form errors announced programmatically, and axe-core running in continuous integration on every pull request. Automated tooling catches roughly half of WCAG failures, so we pair it with manual screen reader passes on VoiceOver and NVDA and with keyboard-only walkthroughs of each critical path.

The California Privacy Protection Agency's regulations were approved by the Office of Administrative Law on September 22, 2025 and took effect January 1, 2026, covering risk assessments, annual cybersecurity audits, and consumer rights around automated decisionmaking technology. The obligations phase in. Risk assessment duties started January 1, 2026, with an attestation and a summary due to the Agency by April 1, 2028. Cybersecurity audit certifications are staggered by revenue: April 1, 2028 for businesses above USD 100 million, April 1, 2029 for USD 50 to 100 million, and April 1, 2030 for those under USD 50 million. Businesses using ADMT to make significant decisions must comply beginning January 1, 2027. Significant decisions are framed around outcomes such as financial or lending services, housing, education, employment or contracting opportunities and compensation, and healthcare services, which is exactly the territory enterprise platforms occupy. Employment is doubly covered, because the California Civil Rights Council has separately extended the Fair Employment and Housing Act regulations to automated-decision systems used in hiring, screening, and promotion, so an ADMT surface inside an HR platform answers to an anti-discrimination regime as well as a privacy one, with selection criteria and the underlying data retained as evidence. We build the pre-use notice, opt-out path, explanation surface, and retention as product features.

Yes, and the two regimes drive different architecture. For health systems such as UCSF Health, California Pacific Medical Center, Dignity Health's San Francisco hospitals, and Zuckerberg San Francisco General, we work under a signed business associate agreement, keep protected health information in-region, encrypt in transit and at rest with customer-managed keys where the client requires it, and enforce minimum-necessary access with per-record audit logging. California layers the Confidentiality of Medical Information Act at Civil Code section 56 on top of HIPAA, and clinical AI adds SB 1120, Chapter 879, which bars an AI tool from denying, delaying, or modifying care based on medical necessity, and AB 3030, Chapter 848, which requires disclaimers and a route to a human on generative AI patient clinical communications unless a licensed provider reads and reviews first. Epic integration runs over HL7 v2, FHIR R4, and SMART on FHIR. For financial institutions the driver is Gramm-Leach-Bliley and the FTC Safeguards Rule at 16 CFR Part 314, with SEC and FINRA retention on communications and records, PCI DSS v4.x scope minimisation through tokenisation, and immutable audit trails. Both get the same underlying discipline: explicit authorization models, tamper-evident logs, tested restores, and evidence packaged for an external assessor rather than assembled in a panic.