The algorithm works. The product path does not.
Turn reference behavior into a partitioned, measurable FPGA, firmware and host system.

AI-ACCELERATED · OUTCOME-PRICED · U.S.-BASED
Built inside your architecture, repositories and review cadence. Systems, algorithms, MATLAB/NumPy models, FPGA, DSP/SDR, firmware, emulation and validation converge under one accountable U.S. delivery boundary, with evidence, runbooks and operating knowledge transferred to your team.
OUTCOME-PRICED DELIVERYFixed front-loaded foundation + back-loaded evidence trancheExplore the model →Silicon, space, medical, sensing, communications and test systems
Serving customers since 2011 · 15 years and counting
WHEN MAGIC LENS FITS
Turn reference behavior into a partitioned, measurable FPGA, firmware and host system.
Make emulation or FPGA prototypes repeatable and useful for real firmware and workloads.
Connect requirements to scenarios, tests, evidence, release configuration and accountable sign-off.
DEEP DSP LINEAGE
Three decades at the DSP-to-hardware boundary, from Viterbi, FIR, modem, baseband and wireless test through cloud FPGA radio, RFSoC sensing, radar, resilient navigation and autonomous platforms.
Explore DSP, SDR and mission platforms →THE EVIDENCE THREAD
AI agents draft and transform bounded artifacts. Deterministic tools measure them. Independent critique challenges assumptions and trace gaps. Accountable engineers approve architecture and acceptance.
Capture operating scenarios, sensor and downlink boundaries, failure consequences, environmental constraints and the evidence required for mission acceptance.
INDUSTRY SYSTEMS
Select a system to see how its architecture, platform profile, failure model and product gate change the engineering plan.

Rate margin · fault recovery · telemetry integrity

Numerical correlation · deterministic transport · fault state

Latency budget · scenario replay · recovery

Reproducibility · workload execution · debug closure

Golden-vector correlation · spectrum awareness · bounded autonomy

Queue ownership · tail latency · fault recovery
Concept illustrations, not representations of client hardware.
PRIOR PROGRAM EVIDENCE
Reference model, RTL/HLS, PCIe/AXI/XDMA, host C, RISC-V/Linux and target demonstration were closed as one system.
Acceptance: correlated end-to-end demonstration and a surgical-robotics licensing program.Camera acquisition, processor data movement, encryption, communications and board control on a Stratix 10 payload.
Acceptance: complete payload data-path behavior on target hardware.Platform architecture, workflow, board delivery, customer evaluations and technical enablement.
Outcome: major semiconductor adoption and company acquisition.AI-GENERATED · AUDITABLE · TESTABLE
AI can draft requirements, UML/SysML views, interfaces, implementation and tests. Persistent IDs, versioned source, deterministic checks and human release authority keep the result traceable through failure analysis and root-cause confirmation. For flight, defense and invasive medical systems, generated code is never accepted as its own audit evidence.
See the sequence, state and evidence models →Specifications, transformations, tests, documentation, triage hypotheses and repeatable workflows.
Compilation, lint, formal, simulation, emulation, profiling, target execution and correlation.
Trace gaps, unsafe assumptions, unreachable states, interface conflicts and unsupported conclusions.
Architecture, risk disposition, IP boundaries, waivers, release configuration and product gates.
SHARED DELIVERY RISK
In the AI era, implementation volume is not value. We price defined delivery and accepted outcomes, not hours, and will put a meaningful portion of fees at risk when evidence and dependencies can be governed.
A fixed, front-loaded engagement establishes the requirement baseline, reference behavior, partition, risk register and acceptance plan.
A fixed foundation fee secures the delivery commitment and covers execution. The balance is back-loaded and earned when pre-agreed evidence gates pass.
Stand up the flow, CI, emulation or validation capability; operate it to stability; transfer documented ownership to the client team.
Each risk-share uses short evidence gates, named dependencies, a test-backed acceptance process and change control. It never promises certification, silicon success or commercial results outside our control. Compare all engagement models and safeguards →
START WITH THE BOTTLENECK
Share the program boundary, current evidence and timing pressure. Do not include confidential design information.
info@magiclenstech.comSend a short note about the milestone and the timing pressure, and we will set up a technical conversation.
Email info@magiclenstech.com ↗We usually reply within one business day.