Kiln
Studio production for your Sunday morning.
Kiln uses deep learning to transform amateur stereo worship recordings into professionally produced audio in minutes, while preserving every note of the original performance and using the weekly church environment as a uniquely stable proving ground.
The singer still sounds like your singer. The band still sounds like your band. The performance is sacred. Only the production changes.
Paired alignment, frozen music understanding models, differentiable DSP constraints, and band-split residual networks work together to lift the production without hallucinating a new performance.
Church livestream audio is emotionally true and sonically underproduced.
More than 300,000 churches in the United States stream worship services every week. Most rely on volunteer operators, modest equipment, and rushed workflows, so the recordings that live forever on YouTube, podcasts, and church apps rarely sound like the room actually felt. That same weekly cadence also makes churches an unusually stable environment for proving production-transfer systems in the real world.
The gap is production, not mastering.
Existing AI mastering products can polish an already coherent mix. They cannot recover lead-vocal presence, rebalance the rhythm section, reshape reverb, or restore section-level dynamics when the original stereo bus is underproduced.
Churches are the proving ground, not the ceiling.
A mix engineer costs roughly $500-$2,000 per service. Specialized processing chains cost tens of thousands of dollars. Training a volunteer team to hear like a producer takes years. Churches matter not only because the need is urgent, but because recurring rooms, teams, consoles, and service formats create a rare environment to validate whether production transfer actually works.
Transfer the production, not the performance.
Kiln learns the difference between volunteer livestream production and high-quality release-ready production from aligned reference examples collected in a repeatable worship environment. It applies those production characteristics to new stereo recordings while anchoring the output to the original vocalist, band, phrasing, and timing.
What changes
Vocal clarity, drum impact, bass control, stereo width, reverb character, and section-level dynamic motion.
Source balance and automation that a traditional one-shot mastering chain cannot infer from a compromised stereo bus.
A delivery path that works first on recorded livestreams, then extends to real-time live-bus processing.
What stays fixed
The singer still sounds like your singer, not a synthetic replacement.
The band still sounds like your band, with the same notes, timing, and arrangement.
The system is explicitly designed to avoid content hallucination and keep the performance sacred.
A stereo-to-stereo transfer stack designed around preservation.
This is the technical core of Kiln. The problem is not generic mastering and not text-conditioned generation. It is constrained audio domain transfer between an underproduced stereo performance and a professionally produced reference style, validated first in a highly repeatable church setting.
Cross-performance paired training
A unique dataset is built from repeatable church recordings and aligned professional production references using chroma-based dynamic time warping and Whisper-based lyric alignment, creating usable supervision from real-world performance pairs.
Asymmetric preservation and quality losses
Training optimizes toward professional production targets while anchoring identity and performance to the input with frozen pretrained music understanding models such as MERT and speaker-verification embeddings.
Differentiable signal processing constraints
Learned EQ, compression, reverb, and spatial operations are implemented in differentiable DSP chains so the output remains a parametric transformation of the input, physically constraining hallucination risk.
Band-split residual production transfer
Source-aware residual networks handle the production changes that fixed parametric processing cannot fully capture, including rebalancing, reverb reshaping, and section-aware dynamic automation.
Long-form paired audio training, frozen perceptual encoders, multi-band residual models, and minute-scale stereo inference are computationally expensive. Kiln training targets NVIDIA A100 GPUs today and is being designed toward real-time inference on NVIDIA hardware for the live processing phase.
Match church livestream recordings with high-quality production references, then align them in time with chroma and lyric cues.
Use frozen music-understanding models to encode production quality targets separately from performance identity.
Apply differentiable DSP layers that guarantee the render remains derived from the original signal rather than invented content.
Band-split residual networks recover the source-aware production changes that static DSP alone cannot reach.
Output a version that translates like a record while keeping the original voice, arrangement, and emotional timing intact.
A stable wedge market with much larger upside.
Churches are the right place to prove this system because they combine repeated need, stable environments, and a rare dataset opportunity. If Kiln works there, the same core production-transfer stack can expand into a much broader live-audio market.
US churches are livestreaming weekly, creating recurring need and unusually consistent operating conditions.
Fixed rooms, repeat volunteer teams, recurring service formats, and week-over-week capture make churches a rare real-world validation environment.
That operational stability creates a unique opportunity to learn production transfer from repeated performances under comparable signal-chain conditions.
If the approach proves in churches, the same infrastructure can extend into broader live music, event capture, creator sessions, and other repeatable stereo production workflows.
Three phases from upload workflow to live-stage intelligence.
The roadmap starts with a narrow, commercially useful production-transfer workflow and expands only where the data and inference stack justify it.
Offline production transfer
Upload a recorded Sunday livestream, run Kiln in the cloud, and receive a professionally produced stereo version in minutes.
Real-time stereo-bus inference
Move the same production transfer concepts onto the live stereo bus so the room and the stream improve together during the service.
Opt-in production augmentation
Add AI-generated ambient pads and textures that complement the band without replacing the live performance, only when the operator wants them.
Built by an operator who lives the problem.
Kiln sits at the intersection of music production, systems engineering, and applied ML. The founder context matters because the failure mode here is not a generic audio app. It is a very specific workflow and quality gap experienced every Sunday.
Founder
The founder combines formal music training, more than a decade of enterprise systems experience, and active firsthand involvement in worship music leadership.
He previously built and shipped RoomScore under Resonant Labs LLC, combining deterministic acoustic measurement with AI synthesis for conference room assessment. Kiln extends that same trust-first approach into music production transfer.
Execution posture
- Product and data strategy grounded in a firsthand weekly workflow, not a speculative category thesis.
- Experience shipping real software, integrating cloud services, and building defensible measurement datasets.
- Technical depth across audio production, signal processing, systems architecture, and applied machine learning.
Looking at AI audio infrastructure, not another plugin.
Kiln is an ML-first production system aimed at a stable initial wedge with clear before-and-after outcomes and much larger adjacent market potential once the transfer quality is proven in the field. Private demos are available on request.