Sourcegraph VS Tacit AI

Sourcegraph's answer

Quinn Slack and Beyang Liu founded Sourcegraph in 2013. They met at Stanford and later worked together at Palantir, where they saw how hard it was for developers to understand large enterprise codebases. Engineers at Google had strong internal code search, and almost nobody else did. Sourcegraph set out to give every company that ability. It started as universal code search, grew into code intelligence and large-scale automated changes, and now provides the context layer for AI agents. The pitch has stayed the same throughout: AI makes it faster to write code, which makes knowing what’s in your codebase more important, and Sourcegraph is the source of truth for that code.

Tacit AI's answer:

In 1996 the anthropologist Julian Orr showed that Xerox's official documentation had drifted away from what people actually saw in the field. The real knowledge was tacit: in people's heads and in their war stories. Thirty years later FMEAs have the same problem, from design through process to the plant floor. They are written once for the audit and slowly go out of date. Design never hears what the field learned, and the engineers who know better retire. Tacit AI is named after that knowledge. We built it to turn what is scattered across specs, process documents, manuals, failure reports and engineers' experience into design, process and equipment risk analyses that stay connected to the evidence.

Sourcegraph's answer

• 1Password
• Indeed
• Canva
• Stripe
• Dropbox
• Hubspot
• BlackStone
• Mistral
• Reddit
• MathWorks

Tacit AI's answer:

• A Top 10 global pharmaceutical company
• A Top 20 global automotive manufacturer
• A global mining company
• A global chemicals manufacturer
• 12 other manufacturers across automotive, pharma and industrial sectors

Sourcegraph's answer

Most alternatives are tied to one code host or are coding assistants that only see the repo in front of them. Sourcegraph sits beneath those tools rather than replacing them. It makes Claude Code, Cursor, Copilot, and Codex more accurate by giving them context from the whole codebase.

Tacit AI's answer:

It works from your data, not a generic library: the FMEAs come from your own designs, processes, equipment and failure history, aligned to AIAG-VDA, SAE J1739, IEC 60812 or ISO 14971 and exported to your own template. It covers the whole lifecycle in one tool: DFMEA with DVP&R and special characteristics, PFMEA with control plans and work instructions, and FMECA with criticality and RCM, all linked to each other. Engineers review drafts instead of writing from scratch: the first FMEA comes in under 3 weeks, similar ones in days, and 80-90% is draft-ready on first delivery. It runs inside your own AWS or Azure cloud, connects to the PLM, QMS, ERP and maintenance systems you already use, and imports and exports with APIS IQ and PLATO e1ns, so you keep your system of record.

Sourcegraph's answer

Large engineering organizations with big, sprawling codebases spread across many repos and code hosts, often in regulated industries like financial services. The buyers are the VP of Engineering (productivity and AI ROI), the Head of Platform/DevEx (developer tooling, migrations, agent infrastructure), and the CISO (finding vulnerabilities and fixing them across the codebase). Day-to-day users are developers, platform teams, and increasingly AI agents.

Tacit AI's answer:

Engineering teams that own risk analysis across the product lifecycle: design engineers writing DFMEAs for new products, process and manufacturing engineers writing PFMEAs and control plans, and reliability engineers writing equipment FMECAs, plus the quality leads who answer for all three in audits and customer reviews. Our core markets are automotive OEMs and Tier 1/2 suppliers working to AIAG-VDA and customer-specific requirements, pharma, and chemicals. The product is also built for aerospace, medical devices (ISO 14971) and mining.

Sourcegraph's answer

• Coverage: it searches across all code hosts at once, including sub-branches, which GitHub’s native search can’t do.
• Permissions: it syncs access rules from each code host, so people and AI agents only see code they’re allowed to see.
• Two kinds of results: Code Search gives exact, repeatable matches, and Deep Search adds agentic investigation with cited sources.

The MCP Server gives the same context to whatever AI agents a team already uses, and Agentic Batch Changes turns a plain-language request into pull requests across hundreds of repos.

Tacit AI's answer:

Most FMEA tools help you fill out the form once, and newer AI tools fill it out faster. Tacit AI also keeps it current. It builds DFMEAs from design specs and requirements, PFMEAs from process documentation, and equipment FMECAs from manuals and failure reports. When a design, a process or the field data changes, the affected rows change with it. Design, process and equipment risk are linked in one place: severity flows from the DFMEA into the PFMEA, and equipment risk from the FMECA feeds into the PFMEA. Every row is traced to the document or report it came from.

Tacit AI's answer:

Large language models running on the customer's own cloud (AWS Bedrock or Azure OpenAI), a knowledge graph linking design, process and equipment risk, statistical reliability models (Weibull analysis, reliability block diagrams), and AI agents that read unstructured engineering text: design specs, process documentation, equipment manuals and failure reports. The architecture is model-agnostic, so a better model can be swapped in without rebuilding the customer's data.