How does ActionBoard’s AI find the information it needs, exactly when it needs it? The
secret is our Dual Retrieval System, a fancy term for a simple but powerful idea: we use two
complementary methods to fetch knowledge – one from structured data (the ActionGraph we
discussed) and one from unstructured or textual data (think documents, chat history, notes). By
combining them, the AI “thinks” in stereo, so to speak, pulling answers from the best of both
worlds. This dual approach is key to achieving the true intelligence we’ve been touting, because
it ensures the AI has both the precise, factual grounding of structured knowledge and the
flexibility and richness of broader information. Let’s break down how it works and why it’s so
effective.
Imagine you have a question: “What’s the status of our Q3 Marketing Campaign and are there
any risks?” Answering this thoroughly means checking the project’s tasks and metrics
(structured info) and possibly scanning meeting notes or comments for nuance (unstructured
info). ActionBoard’s dual retrieval handles this seamlessly. First, the Graph Retriever queries
the ActionGraph: it might traverse the graph to find all tasks under “Q3 Marketing Campaign,”
see which are completed, which are pending, and identify any that are flagged as delayed or
dependent on delayed items. This gives a concrete factual snapshot (e.g., 8 of 10 tasks done, 2
running late, risk node attached to one task). At the same time, the Vector Retriever swings into
action on unstructured data: it converts your question and all relevant documents/notes into highdimensional vectors and finds semantically relevant text. Perhaps last week’s meeting minutes
mentioned a potential risk about ad spend or a client comment indicating concern. The vector
search might pull a snippet like, “Client X expressed concern about timeline for Campaign
deliverables in last call,” which isn’t a structured field anywhere, but is valuable context.
The magic happens when we fuse these results. ActionBoard’s AI merges the hard data from the
graph with the soft insights from text to give you a comprehensive answer: “Campaign is 80%
complete (8/10 tasks). Two tasks are behind schedule (design rollout, copy review), which puts
the final launch at risk medium.com. Notably, in the latest client call, client expressed concern
about the timeline. Recommend reallocating design resources to catch up.” The inclusion of that
client call detail makes the answer far richer – and it was pulled via vector retrieval from
unstructured notes. The structured part made sure the factual status and task list were accurate.
Neither approach alone would be as good; together, they provide a complete picture.
Why did we adopt this dual method? Because research and our own trials showed that it
dramatically improves performance and reliability of AI responses. One recent innovation in AI,
described as a graph-based retrieval augmented system, saw up to 86.4% better performance in
answering complex queries when combining a knowledge graph with traditional
retrieval medium.com. In our internal tests, we observed similar leaps in quality. The knowledge
graph (ActionGraph) reduces irrelevant info and grounds the AI in the exact project data, cutting
down the “token bloat” by orders of magnitude (one study reports a 99% reduction in tokens
needed by using graphs to focus the AI medium.com). Meanwhile, the vector search ensures
nothing important in free-form text is missed – it’s our safety net for context that isn’t neatly
filed in database columns.
Another massive benefit is reducing AI hallucinations. Hallucinations are when an AI just
makes stuff up because it doesn’t actually know the answer but tries to guess. By always
checking the ActionGraph, our system has a built-in fact-checker. If the AI tries to say “All tasks
are on track” but the graph says two are delayed, the system cross-corrects. There’s compelling
evidence that this kind of ensemble approach slashes error rates. In one case study, using a
knowledge graph alongside standard AI retrieval cut the AI’s misinformation (hallucination) rate
from 22% down to 5% arxiv.org. We’ve observed similarly that ActionBoard’s answers about
project status or recommendations align with reality far more often than a naive AI bot plugged
into your data would. The graph component enforces a level of truthfulness, while the vector
component provides nuance and breadth.
So what exactly is the vector side searching? It could be project descriptions, task comments,
team chat logs (if integrated), requirement docs, essentially any textual data you’ve allowed
ActionBoard to index. We use state-of-the-art embedding models (yes, the same kind of tech
behind GPT’s understanding of language) to transform those documents into mathematical
vectors that capture their meaning. When you ask something or when the AI needs extra context,
it transforms the query similarly and finds which documents have the closest vectors – meaning
they’re talking about related topics. This way, even if you phrased something differently (“Any
concerns about Q3 campaign?” vs “risks for Q3 campaign”), the semantic search can find
relevant info (maybe a note that says “issues for marketing project” would still be found). The
beauty is, this doesn’t require you to perfectly tag or organize all your documents; the AI can
infer relevance by meaning.
On the structured side, our system uses graph query languages (like a form of Cypher or
GraphQL) to extract exactly the data needed. For instance, it might run a query: “Find all tasks in
Project X with status = delayed or dependency on delayed” to quickly pinpoint problem areas. Or
“fetch all tasks assigned to Alice due this week” if you ask a question about Alice’s workload.
These structured queries are lightning-fast and precise – no ambiguity there. It’s like asking a
database a question and getting an exact answer. The AI then contextualizes that answer in
human-friendly form and augments it with any relevant narrative from the unstructured side.
A concrete example from a user’s experience: A product manager asked ActionBoard, “What did
we decide about the beta release timeline in our last meeting?” The answer she got was
impressive. The system pulled the decision from the meeting notes (“Team agreed to push beta
release to October 15th to accommodate extra testing”) via vector search, and it cross-referenced
the project’s timeline in the ActionGraph, updating the response with “this is a 2-week delay
from the original plan, and will shift the final launch milestone accordingly.” The combined
answer saved her from digging through notes and manually recalculating timelines. It was all
there in one response, and importantly, grounded in actual data and records. This illustrates
the synergy: the notes alone wouldn’t remind her about the impact on the final milestone, and the
graph alone wouldn’t know the reasoning discussed in the meeting. Together, she got the full
story in seconds.
From a system design perspective, orchestrating this dual retrieval was non-trivial. We had to
build what we call the Fusion Module that takes the graph results and the vector results and
merges them intelligently. It’s not just a copy-paste; it often involves ranking which info is most
important, reconciling any discrepancies, and forming a coherent narrative. In many ways, this
fusion module is like a mediator between a very strict librarian (graph) and a creative researcher
(vector) – it listens to both and decides the best answer. When done right, users shouldn’t even
realize there were two separate retrieval processes; they just get one useful answer.
The impact of this approach on user experience has been profound. Users feel like the system
“just knows” their project context intimately (that’s the graph at work) and also remembers all
the conversations and documents (that’s the vector search at work). One could almost liken it to
the left brain-right brain analogy: one side logical and structured, the other holistic and languageoriented. By having both, ActionBoard’s AI is well-rounded.
We’re not the only ones excited about this; the tech community is abuzz about hybrid AI
systems. Gartner, in fact, emphasizes that such people+machine collaborative approaches are
the key to the future projectmanagement.com – and we extend that to machine+machine
collaboration (different AI techniques helping each other) as a key to AI’s future. The dual
retrieval system is our implementation of that ethos.
With this inside look, you now know how ActionBoard’s intelligence operates on a technical
level: an ActionGraph providing structured context, a dual retrieval system fetching both graph
data and unstructured info, and a fusion process delivering coherent insights. In the next blog,
we’ll have a conversation with the architect of these systems – the lead engineer behind
ActionGraph and dual retrieval. He’ll share the challenges faced and the breakthroughs achieved
in building this platform. If you’re curious about the human effort and vision that made these AI
features a reality, you won’t want to miss it
