XALEN Custom Silicon
ROADMAP 2026 — 2028

Custom Silicon
for Sacred
Intelligence

From software to silicon. XALEN is building custom inference chips optimized for faith-domain workloads: quantized Vedika models running on Indian-manufactured wafers at 100x throughput and 1/10th cost.

PHASE 1 — NOW (LIVE)
Software-Defined Intelligence
200+ models through one API. Custom Vedika model with weight-pruned architecture: 60% cheaper, 3x faster, zero accuracy loss. Running on wafer-scale compute infrastructure today.
200+
AI models available
LLM + Vision + Audio + Image
2,140
tokens/sec (Vedika Fast)
On wafer-scale compute
50ms
first token latency
P50 under production load
91%
citation accuracy
Verified against classical texts
PHASE 2 — Q3 2026
Vedika Quantized: INT4/INT8 on Commodity Hardware
Domain-specific quantization that loses nothing. General LLMs lose 15-30% accuracy when quantized below FP16. Vedika's weight-pruned architecture means the remaining weights are ALL load-bearing, so quantization to INT4 preserves 99.2% accuracy because there's no dead weight to corrupt.
INT4
quantization target
4-bit weights, 8-bit activations
99.2%
accuracy retained
vs 70-85% for general LLMs
4x
memory reduction
Runs on consumer GPUs
10x
batch throughput
Same hardware, 10x more queries
XALEN Silicon
THE WAFER-SCALE ADVANTAGE

Why wafer-scale changes everything

Our wafer-scale compute partner delivers 4 trillion transistors on a single die. The entire wafer IS the chip. No interconnect bottlenecks. No memory wall. Vedika models run entirely on-chip with 44GB on-die SRAM. Zero off-chip memory access. This is how we hit 2,140 tok/sec while others struggle at 500.

PHASE 3 — 2027
XALEN Inference ASIC: Purpose-Built Silicon
A custom inference chip designed from the transistor level for domain-specific workloads. Not a general GPU repurposed for AI. Not a TPU designed for training. A chip that does one thing: runs Vedika-class models at maximum efficiency.
ASIC
Application-Specific Integrated Circuit
Not GPU. Not TPU. Purpose-built.
100x
throughput vs A100 GPU
On domain-specific workloads
1/10th
cost per inference
Fixed function = max efficiency
5W
power per inference
vs 300W on A100 GPU

Why this works for domain-specific AI

General AI chips must support every possible model architecture, every possible precision, every possible workload. That generality costs 10-100x in silicon area, power, and latency. XALEN's ASIC eliminates all that overhead. The chip knows exactly what model it's running (Vedika INT4), exactly what data format (faith-domain embeddings), and exactly what output shape (structured + natural language). Every transistor is doing useful work. Zero waste silicon.

PHASE 4 — 2028
Made in India: Fab Partnership & Sovereign AI
India's semiconductor mission (₹76,000 crore) is building fabs. XALEN will be among the first AI companies to manufacture custom inference chips on Indian soil. Complete sovereignty, from ancient knowledge to modern silicon, entirely Indian.
28nm
initial process node
Proven, cost-effective for inference
Tata
fab partner (Dholera)
India's first semiconductor fab
$0.001
per 1K tokens target
1000x cheaper than cloud GPU
100%
Indian sovereign stack
Knowledge → Model → Chip → Fab
Timeline

From API to ASIC

FEB 2025 — NOW
Software Layer Live
200+ models, 600+ endpoints, 31 languages, Voice AI. Running on proprietary wafer-scale compute. Active enterprise customers across India. Revenue growing month-over-month.
Q3 2026
Vedika INT4 Quantized Release
Domain-specific quantization. Run Vedika on consumer GPUs. 4x memory reduction, 10x batch throughput. Open weights for edge deployment.
Q1 2027
ASIC Tape-Out
Custom inference chip design complete. First silicon samples from foundry. Verification against Vedika INT4 golden model.
Q3 2027
XALEN Inference Chip v1
Production silicon. 100x throughput vs A100. Deploy in XALEN data centers. Customers see 10x cost reduction automatically.
2028
Made in India Silicon
Fab partnership with Tata Dholera or ISMC Mysore. First AI inference chip manufactured entirely on Indian soil. Sovereign AI stack complete.
2029+
Edge Deployment: Temple Chips
Edge inference modules for temples and spiritual organizations. Run Vedika models locally with offline capability. Pre-configured hardware with OTA updates. Pricing based on volume partnerships.

The full stack.
Software to silicon.

Start building on the software layer today. When the silicon arrives, your code doesn't change. It just runs 100x faster.

Start Building → Talk to Sales