Build expert-level AI assistants trained exclusively on your niche, proprietary data to augment and scale subject matter expertise.
Services

Build expert-level AI assistants trained exclusively on your niche, proprietary data to augment and scale subject matter expertise.
We engineer highly specialized AI assistants that act as force multipliers for your most valuable asset: proprietary knowledge. Trained directly on your domain-specific data—be it biochemical research, aerospace engineering, or legal precedents—these systems deliver expert-level guidance, reducing reliance on scarce human experts and accelerating decision cycles.
Deploy a domain-specific language model (DSLM) in 4-6 weeks, achieving >90% accuracy on internal validation tasks and cutting SME query response time from days to seconds.
Our development process:
This service is part of our broader Enterprise AI Copilot Customization pillar. For related capabilities, explore our work on Legacy ERP AI Copilot Integration and Secure Internal AI Assistant Deployment.
Our domain-specific AI assistant development delivers concrete business value by embedding expert-level intelligence directly into your workflows. We focus on quantifiable improvements in efficiency, accuracy, and cost.
We deliver AI assistants fine-tuned on your niche corporate data—pharmaceutical research, aerospace engineering, legal precedents—achieving over 95% accuracy on domain-specific queries, dramatically reducing reliance on scarce subject matter experts.
Deploy assistants with data processing confined to your sovereign infrastructure, ensuring full compliance with the EU AI Act, FedRAMP, and internal IP protection mandates. All inference occurs within your air-gapped or localized environment.
We engineer AI overlays that integrate directly with proprietary ERPs, custom databases, and legacy software without costly migrations. Achieve a functional proof-of-concept integrated with your core systems in under 4 weeks.
Our assistants leverage optimized Small Language Models (SLMs) and efficient RAG architectures, delivering sub-second response times with up to 80% lower cloud compute costs compared to generic LLM APIs, ideal for high-volume internal use.
Move beyond chat to assistants that execute tasks. We build copilots that autonomously complete multi-step processes—generating reports, querying data warehouses, updating CRM records—reducing manual workflow time by an average of 60%.
Gain full visibility with built-in audit trails, data lineage tracking, and policy-as-code enforcement. Our deployment includes governance dashboards for monitoring usage, bias, and compliance, aligning with NIST AI RMF frameworks.
A transparent breakdown of our phased approach to building a secure, high-accuracy domain-specific AI assistant, from initial data assessment to full-scale deployment and ongoing optimization.
| Phase & Deliverables | Timeline | Key Activities | Client Involvement |
|---|---|---|---|
Phase 1: Discovery & Data Audit | 1-2 weeks | Requirements workshop, proprietary data source cataloging, security & compliance review, initial architecture proposal. | Provide data access, key SME interviews, finalize success metrics. |
Phase 2: Data Pipeline & Model Strategy | 2-3 weeks | Build secure data ingestion pipelines, design semantic chunking strategy, select & fine-tune base model (e.g., Llama 3.1, GPT-4), establish evaluation framework. | Approve data processing approach, validate initial model outputs against test queries. |
Phase 3: Core RAG & Assistant Development | 3-4 weeks | Develop vector database architecture, implement retrieval-augmented generation (RAG) system, build conversational interface, integrate with first internal API/data source. | Weekly review of assistant capabilities, provide feedback on accuracy and usability. |
Phase 4: Pilot Deployment & Validation | 2 weeks | Deploy to limited user group (e.g., 10-50 SMEs), conduct structured testing, measure hallucination rate & accuracy, perform security penetration testing. | Select pilot users, facilitate testing sessions, collect and prioritize feedback. |
Phase 5: Scaling & Integration | 2-3 weeks | Scale infrastructure for enterprise load, integrate with additional internal systems (ERP, data warehouse), implement advanced features (multi-agent workflows, analytics dashboard). | Coordinate with internal IT for system integrations, approve go-live plan. |
Initial Go-Live & Handoff | 1 week | Full production deployment, administrator training, delivery of technical documentation & source code, establishment of monitoring alerts. | Confirm production readiness, complete admin training, sign-off on deliverables. |
Ongoing Support & Optimization | Ongoing | Performance monitoring, quarterly model retraining with new data, continuous accuracy improvement, SLA-backed support. | Provide updated domain data, participate in quarterly review sessions. |
We build AI assistants that master your proprietary data and workflows, delivering expert-level guidance and operational efficiency where it matters most.
Develop clinical decision support systems and ambient AI for documentation, trained on proprietary medical literature, EHR data, and clinical trial protocols to reduce clinician burnout and accelerate research. Our solutions ensure HIPAA compliance and integrate with legacy healthcare IT systems.
Engineer AI assistants for real-time fraud detection, algorithmic risk modeling, and autonomous compliance auditing. Trained on proprietary transaction histories and regulatory frameworks, they deliver deterministic insights for high-speed trading desks and personalized retail banking.
Build AI copilots for contract lifecycle management, predictive litigation analysis, and automated regulatory compliance. Trained on your firm's precedent database and jurisdictional laws, they parse complex legal language with human-in-the-loop safeguards for critical decisions.
Integrate industrial AI copilots for predictive machine maintenance, automated quality inspection via computer vision, and supply chain visibility. These assistants act as intelligent overlays on legacy SCADA and ERP systems, providing real-time diagnostics and operational guidance.
Develop secure, air-gapped AI assistants for engineering design, maintenance logistics, and geospatial intelligence analysis. Trained on proprietary schematics and classified data, they operate within sovereign AI infrastructure to meet ITAR and other stringent security mandates.
Deploy AI for grid optimization, predictive maintenance of transformers, and energy demand response platforms. Our assistants analyze sensor telemetry and historical failure data to forecast equipment issues weeks in advance, optimizing for the demands of hyperscale computing.
Get clear, technical answers to the most common questions about building specialized AI assistants for proprietary corporate data.
Contact
Share what you are building, where you need help, and what needs to ship next. We will reply with the right next step.
01
NDA available
We can start under NDA when the work requires it.
02
Direct team access
You speak directly with the team doing the technical work.
03
Clear next step
We reply with a practical recommendation on scope, implementation, or rollout.
30m
working session
Direct
team access