Governance.
Shared rules applied the same way in every team, every site, and in CI.
You are measured on predictable delivery.
Standardize the front end without changing the toolchain
Review loops, onboarding friction and late-stage issues are upstream problems that surface downstream, where they cost the most. Sigasi builds one deterministic model of your SystemVerilog, Verilog, VHDL and mixed-language projects, applies your rules consistently across teams and sites, and runs the same checks in the engineer’s workspace, in a coding agent, and in CI. It runs in VS Code next to the tools you already have.
SystemVerilog · Verilog · VHDL · Mixed-language · Model-neutral and toolchain-independent · Analyzed locally, on your own infrastructure
What a CAD organization gets from one shared project model
Visibility.
One project model, so hierarchy and dependencies are inspectable rather than tribal.
Consistency.
The same diagnostics for the engineer, the reviewer, the agent and the pipeline.
Predictability.
Issues surface while a change is still cheap, instead of at integration.
Fit.
Runs in VS Code beside your simulator, synthesis and sign-off flow. Nothing is replaced.
Standardizing the front end without asking anyone to change tools
Your tools are judged by silicon, not by features.
A CAD organization is measured on something it does not write a line of: how reliably the design teams deliver correct silicon on the promised deadline. Tooling decisions reach that outcome indirectly, through review loops, onboarding time, verification load, and documentation that stopped matching the design months ago.
That is an awkward place to argue from. The failures you are accountable for happen downstream, and the levers you control are upstream.
Where the schedule actually leaks
Four patterns show up in almost every program.
Review loops that make schedules unpredictable, because an issue introduced in the week the code was written is not found until integration or simulation bring-up. Onboarding friction and inconsistent coding patterns, which turn every new hire and every transferred block into mentoring overhead. Late-stage issues that consume verification bandwidth that was budgeted for finding real bugs. And documentation drifting out of sync, so the only reliable description of a block is in the head of the engineer who wrote it.
None of these needs a new methodology. They need the problem to surface earlier, and the same answer to be available to everyone who asks.
One project model, used by every team
Sigasi Visual HDL is a deterministic semantic HDL platform. It builds one model of the real project: types, scopes, libraries, interfaces, hierarchy and dependencies across SystemVerilog, Verilog, VHDL and mixed-language designs.
That model is shown to the engineer in the VS Code workspace, exposed to a chosen coding agent through MCP and the command line, and run in CI. It is the same engine in every place, so the author, the reviewer, the agent and the pipeline work from the same facts rather than from four tools that disagree at the edges.
For a CAD organization that has consequences you can plan around.
Standards that are applied, not published
You can customize linting rules to your taste, easily implementing a company styleguide or adopt standards such as DO-254 or STARC. The team agrees which issues block and which inform, while being able to apply different standards to self-written and third-party souce code.
Productivity that scales with headcount
Accurate navigation and symbol resolution, diagnostics while engineers type, and refactoring that stays project-aware across files and packages. The benefit grows with the size of the codebase and the number of contributors, which is the opposite of how editor features usually scale.
Documentation generated from the source
Hierarchy views, block and state machine diagrams and design documentation are produced from the code and regenerate as it changes. Handovers stop depending on whether someone updated a drawing, and knowledge silos get smaller without anyone being asked to write more.
Rollout that does not fight the toolchain
Sigasi runs in VS Code next to the simulator, synthesis and verification tools your teams already use, and complements them rather than replacing anything. That is what makes a multi-team, multi-site rollout practical: you are not asking engineers to abandon a flow they trust.
Built for long-lived codebases
The designs you support outlive the people who wrote them. Mixed-language environments, large hierarchies, reused IP and many contributors are the normal condition, not an edge case, and they are what the project model is built for.
That also covers the direction most programs are traveling. An FPGA prototype becomes an ASIC-grade flow on the same source. A verification environment in SystemVerilog sits on top of RTL in VHDL. The boundary between them is where project knowledge is thinnest, and it is analyzed as part of one model rather than as two unrelated files.
Where AI fits, and where it does not
Coding agents will reach your teams whether or not you select one. They can draft and modify HDL quickly, and they do not automatically know your library mappings, compile order, include paths, reused IP or team rules.
Sigasi gives the agent that project context and checks what it proposes, through the same engine the engineer uses. The agent does not grade its own work, and the engineer approves what moves downstream. Sigasi does not generate RTL and does not decide.
This is also a procurement point. Sigasi is not tied to one coding agent, one FPGA vendor, one simulator or one vertically integrated EDA suite. One program may approve a cloud agent, another an internally hosted model, another none at all, and the same deterministic layer serves all three. AI is optional. Project understanding is not.
Deployment and data handling
Sigasi analyzes HDL locally, on the engineer’s workstation or inside your controlled environment. Project files are not sent to Sigasi servers for semantic analysis. Commercial telemetry is optional and does not transmit HDL source code, and licensing can run from a node-locked file or an internal FlexNet server. The analysis engine can operate without external internet access when installation, licensing and dependencies are available inside the environment.
A connected coding agent is a separate component with its own provider and configuration, and should be reviewed on its own terms. More detail is on the security page.
What Sigasi does not do
Sigasi does not generate RTL and it does not decide. It does not replace simulation, formal verification, coverage analysis, synthesis, sign-off or your engineers. It does not certify a design or guarantee compliance, and no tool that catches issues early can promise to catch every issue. Checks run when an engineer or an agent invokes them, and in CI wherever your pipeline requires them; capabilities vary by edition.
A pilot you can defend with numbers
Abstract claims are not a procurement case. A short trial on an active repository is.
- Select one or two representative projects.
- Apply your baseline rule set, or start from a standard one.
- Run Sigasi in VS Code, with optional CI checks, for two to three weeks.
- Review the metrics against your own baseline.
The measurements worth taking:
- avoidable issues caught before simulation and CI
- pull request review churn, in iterations and time to merge
- navigation and refactoring time in large designs
- onboarding speed, from day one to the first productive change
- surprises at integration bring-up
Within one project you’ll feel Sigasi’s impact on your productivity and velocity. Request a team trial, or book a free session tailored to your flow.
One project model across every stage and every role
Sigasi parses the full project, not the open file: hierarchy, types, scopes, dependencies, interfaces and references across SystemVerilog, Verilog and VHDL. That shared model is what lets an engineer, a reviewer, a coding agent and your CI ask the same question and get the same answer.
- Project-aware diagnostics while a change is still cheap to make
- Block and state machine diagrams generated from the code, so a block you did not write can be read, not just inherited
- The same project model applied to testbenches and verification code
- Command-line analysis in CI, with configurable failure thresholds your pipeline can act on
- Shared coding rules applied consistently across teams
AI increases speed.
Sigasi enhances efficiency.
Save time
Compile at coding speed, so problems surface in the second they are written rather than at integration.
Save costs
Fewer iterations, less wasted AI tokens, less review late in a program when work is most expensive.
Keep review possible
RTL checked against your real project whether you or an AI agent wrote the code. You decide what moves forward based on facts, not hope.
WITHOUT SIGASI
Total: 38 weeks
RTL coding, review & linting
12 weeks
Simulation and verification
16 weeks
Synthesis and post-verification
10 weeks
WITH SIGASI
Total: 28 weeks
RTL coding, review & linting
6 weeks (-50%)
Simulation and verification
12 weeks (-25%)
Synthesis and post-verification
10 weeks (no impact)
See what your engineers would see
Five short recordings of the product doing the work described above. No sign-up.
View all screencasts
Incremental real-time compiler
Context-aware feedback, linting and completion as you type
Interactive visual design creator
Block and state machine diagrams generated while you type
Dependency analysis
Spot missing mappings and broken includes instantly
Verification accelerator
Debug UVM, UVVM, OSVVM, VUnit and Cocotb faster
Up-to-date documentation
Documentation generated from the design, always currentPilot Sigasi on one active repository
A two to three week trial on one or two representative projects, with your own rule set, in VS Code and optionally in CI. Measure review churn, pre-simulation issues, onboarding speed and integration surprises against your own baseline.