AI strategy with no delivery path
Leadership wants AI initiatives to move, but the team still needs clarity on roadmap, architecture, data boundaries, and operating model.
Strategic AI consulting for companies moving beyond experimentation
Consulting, architecture, and delivery for AI systems that need to perform in production.
NewCo360 helps companies define where AI creates leverage, choose the right architecture, and implement production systems across private AI, RAG, analytics, and distributed infrastructure.
- AI strategy tied to business goals and operating constraints
- Private AI, hybrid cloud, or on-prem deployment paths
- Performance, latency, and cost treated as first-order decisions
What you can hire us for
AI strategy & architecture
Define roadmap, use cases, build-vs-buy, and deployment model.
Private AI & RAG systems
Design secure knowledge workflows and controlled AI experiences.
Performance & cost optimization
Reduce latency, infrastructure waste, and model-serving spend.
Delivery from MVP to production
Ship systems that can operate under real user, data, and infra pressure.
Manifesto
We don’t build demos. We build production systems.
Problems we solve
Where companies usually need help with AI
NewCo360 is most useful when AI needs to move from pressure and expectation into architecture, execution, and reliable operation.
Promising prototypes that do not survive production
Internal demos work until latency, governance, observability, and integration pressure expose the missing engineering layers.
Cost and performance drifting out of control
Serving, retrieval, queueing, and data paths become expensive because the architecture was not designed around measurable budgets.
Need for private AI without slowing delivery
Companies want to keep sensitive workloads under control while still moving fast across hybrid, cloud, or on-prem environments.
Proof & credibility
Built from production systems, not speculative slides.
15+
Production services
70+
Systems built
6.1K+
Automated tests
90%
Infra cost reduction
Private AI
RAG Systems
Distributed Inference
Analytics Engineering
Observability
Services
Service lines built around strategic AI delivery
The primary offer is not a product catalog. It is consulting and engineering work scoped around business goals, architecture decisions, delivery risk, and operating performance.
AI strategy & architecture
Strategic and technical definition of where AI should be applied, how the architecture should evolve, and what should remain private, hybrid, or provider-managed.
- Roadmap for AI initiatives tied to business and operating constraints
- Architecture review covering data flows, deployment paths, and vendor exposure
- Practical build plan for MVP, pilot, or production modernization
Architecture review patterns Private AI references Platform decision framework
Private AI, RAG & knowledge systems
Design and implementation of internal copilots, retrieval systems, AI knowledge workflows, and secure data-aware experiences for enterprise contexts.
- Private AI chat experiences backed by controlled data access
- RAG pipelines with multimodal ingestion and production-grade retrieval
- Persistent memory and structured context for agent and assistant systems
Private AI deployment blueprint Retrieval and ingestion framework Persistent memory layer Secure conversational interface patterns
Performance & cost engineering for AI workloads
Profiling and redesign of AI workloads, cache layers, serving topology, and data paths to improve latency, resilience, and infrastructure economics.
- Latency and throughput diagnostics with measurable remediation plan
- Model-serving cost/performance tradeoff analysis
- Optimization of analytics, NLP-to-SQL, and retrieval-heavy systems
Conversational analytics architecture Benchmarking methodology Usage and observability data layer Distributed inference blueprint
Implementation & product engineering
Hands-on delivery for AI-native products, admin surfaces, orchestration layers, and internal tools that need to move from concept to operational software.
- Solution design and implementation with engineering ownership
- Admin surfaces, SDKs, billing, auth, and operational tooling
- Bots, workflows, and integrations connected to the rest of the stack
Workflow builder patterns Agent orchestration layer Operational console foundation Integration and automation connectors
Engagement models
Ways to work with NewCo360
You can start with strategy, a technical review, a defined delivery scope, or an embedded partnership depending on how mature the initiative already is.
AI diagnostic
Short engagement to assess opportunities, risks, and technical constraints before a larger commitment.
Best when leadership needs a clear starting point, architecture direction, and prioritization.
Architecture review
Deep technical review of a current system, initiative, or vendor setup with a remediation and modernization plan.
Best when a team already has something running, but performance, cost, or platform decisions are unclear.
Implementation sprint
Focused delivery for a defined scope such as RAG, private AI, orchestration, analytics, or infra optimization.
Best when the problem is known and the business needs execution, not another discovery deck.
Embedded engineering partner
Ongoing architecture and engineering support alongside internal product, data, and platform teams.
Best when a company needs continuity from roadmap through production operation.
Approach
How we work
The process is structured to turn strategic intent into a working system with measurable performance and clear operational ownership.
01
Strategic diagnosis
We map business priorities, data topology, latency budgets, security requirements, and operating realities before recommending a path.
02
Architecture and roadmap
We define what to build, what to reuse, what to keep private, and how to sequence the delivery into a practical production plan.
03
Implementation and optimization
We deliver the core system and optimize serving, retrieval, cache, queueing, and storage against measurable performance targets.
04
Operational handoff
You get deployable systems, instrumentation, ownership boundaries, and a roadmap for scale instead of a stranded prototype.
Delivery frameworks
Reusable building blocks that shorten time to value
These are internal NewCo360 frameworks, patterns, and operating components built from real delivery experience. They are used selectively when they improve speed, reliability, and implementation quality.
Private AI foundation
Reusable architecture for sovereign AI chat, internal copilots, provider routing, and controlled access boundaries.
Retrieval and knowledge framework
Document ingestion, retrieval, chunking, and knowledge orchestration patterns for enterprise AI workflows.
Conversational analytics stack
Building blocks for NLP-to-SQL, semantic caching, entity resolution, and conversational access to data.
Distributed inference architecture
Serving patterns for hybrid and private AI workloads where cost, latency, and hardware utilization matter.
Workflow and agent orchestration layer
Coordination layer for tools, workflows, agent collaboration, and operational control surfaces.
Persistent memory services
Context and memory services for AI assistants and agents that need continuity, recall, and session isolation.
Case studies
Measured outcomes, not vague transformation claims
Representative outcomes from the architectures, systems, and optimization work that now inform NewCo360 engagements.
Retail analytics & auction data
Lakehouse + entity extraction for premium wine operations
A domain-driven data platform with NER, matching, and operational pipelines for a premium wine business.
- 95%+ pipeline success
- 1.5M+ records processed
- 40x faster NER
Business intelligence
NLP-to-SQL platform for enterprise analytics
Conversational data access with semantic cache, entity resolution, and chart generation tuned for production response times.
- 431x cache speedup
- ~30ms cache hit
- $117K annual savings
AI infrastructure
Distributed inference with hybrid GPU coordination
Peer-to-peer inference architecture reducing infrastructure cost while maintaining usable latency and throughput.
- 90% infra cost reduction
- ~370ms TTFT
- 69+ tokens/sec
Differentiators
Why teams bring NewCo360 in
The edge is not novelty. It is architecture discipline across strategy, product, infrastructure, data, and performance.
AI sovereignty by design
Private AI, hybrid cloud, and on-prem deployment paths are considered early, before procurement or compliance constraints force rework.
Performance is strategy
Latency, throughput, and cost are treated as business and product constraints, not post-launch cleanup work.
360° system view
NewCo360 works across application, API, model serving, queueing, data, observability, and deployment layers as one operating system.
Proven accelerators, not theoretical frameworks
When it helps delivery, we bring proven internal modules and patterns from chat, RAG, analytics, orchestration, and distributed inference.
Geo SEO
Where we work
NewCo360 operates from Brazil and works remotely worldwide for companies that need AI strategy, architecture, delivery, and performance engineering without heavy agency overhead.
- Brazil
- Remote Worldwide
- Hybrid engagements
- On-prem or private cloud
FAQ
Questions that usually come up before a project starts
Short answers for scope, geography, delivery model, and how NewCo360 packages what is already proven.
NewCo360 is primarily a consulting and solutions firm. We sell diagnostics, architecture work, implementation, and optimization services, using proven internal modules only when they accelerate delivery.
Brazil is the primary local market, but the operating model is remote-friendly and designed for worldwide delivery where timezone overlap and execution fit the engagement.
Yes. NewCo360 can operate as architecture partner, implementation lead, or performance task force alongside internal product, data, and infrastructure teams.
Both. Engagements can start from strategy and architecture, a custom implementation, or a faster delivery path that uses internal accelerators where they make sense.
The delivery approach prioritizes controlled data boundaries, auditable access, and deployment models that can stay inside your infrastructure when needed.
Contact
Bring the strategic problem, the technical bottleneck, or both
Use the form for AI strategy work, architecture reviews, private AI initiatives, performance investigations, or delivery support that needs to move beyond prototype mode.