Context
Agent context platforms
Ingest documents, conversations, tickets, meetings, CRM records, support history, files, and web signal into an AI-ready context layer.
Graphlit Services
AI Architecture & Agent Context Services
Senior architecture and implementation help for teams building agents, copilots, and context-aware AI products.
Graphlit helps software teams get from AI prototype to production with the right context architecture, ingestion strategy, tool-calling design, evaluation plan, and runtime foundation.
The hard part
The model is not usually where AI products get stuck.
If you are building agents, copilots, AI workflows, or context-aware product features, the hardest questions usually are not inside the model. They are around it.
What data should the AI access? How does it stay fresh? Which facts are current? Where did the answer come from? Which tools should the agent use? What should be built now, and what should stay disposable until you learn more?
We help founders and product teams answer those questions early, so they can move faster without painting themselves into a corner.
What we help teams build
AI products where context is the moat and agents are the interface.
Workspace
AI knowledge workspaces
Build search, chat, browsing, and retrieval experiences over private company or personal knowledge across the tools people already use.
Vertical AI
Vertical agent products
Create domain-specific agents for deal intelligence, customer intelligence, support intelligence, research, sales, operations, or compliance.
MCP
MCP-enabled agent systems
Expose context to Codex, Claude Code, Cursor, Claude, internal agents, and custom clients through Model Context Protocol patterns.
Research
Research and briefing engines
Resolve entities, gather facts, evaluate gaps, deepen research, synthesize findings, cite sources, and verify the result before delivery.
Action
Governed agent write paths
Let agents draft email, write documents, post to channels, create tickets, schedule meetings, and append notes with explicit trust boundaries.
Runtime
Durable agent runtime
Design scheduled agents, triggered agents, webhooks, heartbeat checks, workers, retries, side-effect budgets, observability, and return-to-thread delivery.
Graph
Context graphs and enrichment
Extract entities, facts, relationships, timelines, source provenance, external signal, and enrichment data from messy operational context.
From architecture to production
Production agents need more than a clever prompt.
We help teams design the intelligence layer around real workflows: multi-step reasoning, context engineering, tool calling, exception handling, evaluation, feedback loops, governance, and cost control.
The work runs in two registers. With product and operations leaders, we map how work actually happens. With engineering, we turn that into buildable agent specs, success criteria, rollout plans, and implementation decisions.
01
AI opportunity and architecture audit
Map how the product or workflow actually moves across users, systems, data, decision points, and failure modes. Identify where agents can help, where simpler automation is enough, and where time, money, or quality is leaking today.
02
Process architecture and context design
Translate what we find into concrete agent specifications: what the agent does, what data it touches, what success looks like, what failure modes need handling, and what the engineering team can build now.
03
Build sprint or implementation support
Help your engineering team implement the ingestion layer, MCP tools, agent workflow, research engine, data model, or runtime path that gets the product from demo-grade to pilot-ready.
04
Validate, harden, and compound
Monitor accuracy, surface failure modes, tighten quality, safety, groundedness, latency, cost, observability, and governance, then fold what worked back into a reusable pattern library.
Production agent architecture
The missing layers around production agents.
We have already built these layers in Graphlit, Zine, and Dossium. Services help other software teams apply the same patterns to their own products.
1
Context and methodology
Operational context tells an agent what is true. Methodology tells it how your team turns truth into work. We help teams model both.
2
Presence and surfaces
Useful agents are not trapped in one chat box. They need to meet users in web apps, Slack, email, voice, SMS, docs, tickets, and calendars.
3
Governed action
Reading is half the agent. Writing is the other half. We design provider-agnostic work verbs, trust tiers, draft paths, and side-effect controls.
4
Research intelligence
Real research is not one search. Agents need routing, enrichment, entity resolution, evidence gathering, worker decomposition, synthesis, and verification.
5
Runtime and trust
Production agents need durable workflows, queues, budgets, observability, stuck detection, prompt caching, and delivery back to the surface where work began.
How we work
Fast build cycles, serious architecture judgment.
We use the latest coding agents, including Codex and Claude Code, in our own product work. That gives us a practical feel for hyper-fast implementation cycles, agent-assisted refactors, and the new pace of software delivery.
The point is not speed for its own sake. The point is using that speed with enough product and architecture discipline to avoid rebuilding the foundation when pilots start to matter.
AI agent architecture
Agentic RAG systems
Context ingestion pipelines
MCP integration design
Context graph modeling
Entity and identity resolution
Facts, timelines, and provenance
Data enrichment and external signal
Tool-calling and distribution
Agent runtime architecture
Evaluation and observability
AI-assisted implementation
Engagements
Choose the amount of help your team needs.
AI Architecture Kickstart
Starting at $5k
Founders and product teams with a prototype, pilot, or roadmap where the next architecture decisions feel expensive to get wrong.
What you get
- Architecture, product, and roadmap review
- Risk map across context, agents, tools, models, permissions, evals, and cost
- Practical implementation plan for what to build now, defer, or avoid
- Written architecture memo your team can use internally
AI Product Build Sprint
Scoped sprint
Teams that need to accelerate a specific AI product milestone, customer pilot, agent workflow, or context-aware prototype.
What you get
- Product and technical design
- Agent workflow and tool-calling architecture
- Context ingestion and data-flow implementation support
- Production-readiness plan for the next build phase
Fractional AI Architect
Monthly advisory
Companies that need senior AI product and platform guidance but are not ready to hire a full-time AI architect or platform lead.
What you get
- Weekly architecture and product working sessions
- Async support for technical decisions
- Review of implementation plans, data flows, and system design
- Guidance for internal engineering teams as they build
Why Graphlit
We are not approaching AI architecture from the outside.
Graphlit builds the context infrastructure behind AI agents and AI-native applications: multimodal ingestion, entities, facts, relationships, source attribution, search, MCP, and agent context.
Zine and Dossium are product proof. They show how context ingestion, agentic retrieval, MCP, data enrichment, context graphs, governed write paths, and durable runtime patterns become actual user-facing products.
Common questions
The things teams usually need to decide.
Is this RAG consulting?+
Not really. Retrieval is part of the work, but production AI products need more than one vector index. We focus on the architecture around agents: ingestion, context graphs, memory, permissions, provenance, tool use, workflow triggers, evaluation, runtime, and governed action.
Can you help us build something like Zine or Dossium?+
Yes, when the product depends on private, messy, changing context. That can mean a knowledge workspace, a vertical agent product, a research engine, a customer-intelligence system, an MCP-enabled context layer, or a durable agent workflow platform.
Do we need to use Graphlit?+
Graphlit is often the fastest path because it already handles multimodal ingestion, entities, facts, search, conversations, MCP, and agent context. But the engagement starts with the product and architecture problem, not a forced tool choice.
Can you work with our engineering team?+
Yes. The strongest fit is helping internal teams make architecture decisions, unblock implementation, review designs, and accelerate the parts of the system where agent context, tool use, data flows, or runtime boundaries matter.
How do AI coding agents fit into the work?+
We use modern coding agents such as Codex and Claude Code in our own product work, and we are comfortable building with that operating model. Used well, they compress implementation cycles dramatically. The value is pairing that speed with senior architecture judgment so teams move quickly without cementing the wrong foundation.
Work with us
Bring us in when the prototype is starting to get real.
If your team is building an AI product, agent, copilot, or context-aware workflow, we can help you make the right architecture and implementation decisions sooner.