6-Week Pilot to Production
A structured, compliance-first framework accelerating sandbox prototypes to validated enterprise deployments.
Zero-Trust Security & Compliance
Deployable inside your secure cloud infrastructure (VPC, AWS/GCP/Azure) with SOC 2, GDPR, and HIPAA compliance protocols.
78% avg. acceleration in processing throughput
Quantifiable Business Value
Proven acceleration across production ML pipelines
Full-Stack, In-House
Strategy, architecture, development, MLOps, and deployment — one team, zero handoffs.
Deploy Anywhere
Cloud-agnostic infrastructure across AWS, GCP, and Azure. Your stack, your rules.
What we've solved
Real problems.
Real solutions. Real results.
EnergyB2B
Automating Complex Intent-Scoring & B2B Lead Prioritization
An energy enterprise's B2B sales division was experiencing massive inefficiency, chasing leads that matched basic criteria but lacked active intent or budget. High-value sales executives were spending hours qualifying prospects manually. We built an automated qualification pipeline that parses incoming lead signals—including company firmographics, intent behavior, and structural fit. An LLM-driven intelligence layer scores and qualifies each prospect in real time. Only high-intent, qualified opportunities are routed to the CRM for direct sales follow-up, ensuring maximum executive efficiency.
70%
Reduction in customer acquisition cost
90%
Qualification accuracy rating
100%
Resource allocation to qualified intent
Stripestripe.com
High Intent96%
SpamBot LLCfree-gift.ru
Spam4%
OpenAIopenai.com
High Intent98%
SEO Blastseo-agency.net
Low Intent12%
Microsoftmicrosoft.com
High Intent95%
B2BD2C
Autonomous Workflow Orchestration for Multi-Channel Outreach
Following qualification, maintaining a consistent, compliant, and timely outreach cadence across thousands of accounts was key. Deals regularly stalled due to manual pipeline gaps and variable follow-up times. We developed a multi-channel autonomous agent workflow. It schedules, tailors, and triggers outreach based on continuous behavioral tracking—delivering highly contextual email and SMS touchpoints. For B2B enterprise sales, the platform manages the entire nurture cycle, surfacing accounts to representatives only when they indicate active buy signals.
80%+
Automation of touchpoint sequence
8 states
Multi-state production deployment
Zero
Out-of-policy user interactions
Research & publishingRAG
Secure Enterprise RAG System for Citation-Grounded Research Retrieval
R&D teams and academic researchers spent significant time manually querying disparate databases, checking citations, and validating papers. The lack of semantic search across global repositories delayed critical product cycles. We architected a high-throughput RAG search and synthesis engine. Sourced from over 300 million academic papers and clinical trial datasets, it translates natural language queries into grounded, fully cited answers. Researchers receive instant, validated summaries with clear, traceable references to original source materials, accelerating the drug and product discovery pipeline.
300M+
Papers indexed & semantically mapped
Instant
Validated, grounded answers
Core
Enterprise revenue driver
|
SaaSResearch tools
Helping users discover what a product could do for them
A research platform had powerful features that users simply weren't finding or using. Engagement was lower than it should be, and the team had no reliable way to know which features to prioritise — or how to nudge users toward upgrading. We built a system that watches how users move through the product — what they click, what they search, where they stop — and uses that to surface the features most relevant to what each user is trying to do. Users found value faster. The same data gave the product and sales teams a live read on what was resonating, which users were close to upgrading, and where to focus next.
Higher
Feature adoption & engagement
Smarter
Upgrade nudges, less guesswork
Live
Product & sales signals
DataPublishing
Millions of messy records — made clean and trustworthy
A large academic data platform was sitting on millions of records — papers, authors, journals — that were messy, duplicated, and inconsistent. The same author appeared under 12 different names. Journals were listed 40 different ways. Nothing built on top of it could be trusted. We built a pipeline that processed millions of records, identified duplicates, standardised names, and enriched each entry with accurate, consistent information. The result was a clean dataset the platform could confidently build products and recommendations on — with an automated pipeline that keeps it clean as new records come in.
Duplicate Records
Acme Co.+1 (555) 123
CRMAcme Corpinfo@acme.com
BillingAcme, Inc.555-1234
SupportFAQs
Quick answers to the questions every engineering leader asks before we start.
- How long does a typical AI implementation take?
- Our average is 6–12 weeks from kickoff to production. We run a 2–4 week POC first to validate the approach before committing to full build-out.
- Do you work with our existing stack or rebuild from scratch?
- Both. We're experienced with legacy system integration and greenfield cloud-native builds. We'll recommend what's right based on your constraints.
- Who owns the intellectual property (IP) of the deployed systems?
- You own 100% of the custom IP, codebase, and fine-tuned models. We do not lock you into proprietary vendor ecosystems.
Strategic Engineering.
Predictable Delivery.
Tell us what you're trying to build. We'll tell you honestly whether AI is the right tool — and how fast we can ship it.