Stocks Assistant
Most investing tools give users more data. Stocks Assistant gives them understanding. The product is built around a core insight: financial markets produce two distinct streams of signal that rarely get analyzed together — what companies say (earnings calls, management guidance) and what the market bets (options positioning, call/put interest).
The assistant transforms full earnings call transcripts into structured insights, producing a normalized Momentum Score (0–100) across four quarters and a Company Performance Score that filters short-term noise into durability signals. On the market side, it reads options chains to surface positioning intent in plain language: "Bullish: call interest leading put activity."
Why accuracy matters here: By combining transcript intelligence with options data normalized into consistent scoring models, the assistant eliminates the most common investor error — reacting to a single data point in isolation.
Stocks Portfolio AI Agent
Stocks Assistant delivers the intelligence layer. Stocks Portfolio AI Agent takes the next step — turning that intelligence into concrete, personalized portfolio recommendations tied to each investor's actual holdings, goals, and account structure.
The agent begins by understanding the investor: holdings, account types (taxable vs. Roth), target allocations, concentration limits, and time horizon. It then layers in company-level signals to evaluate each position in context — suggesting rebalancing moves, entry and exit timing, and stop-loss logic.
One of its most differentiated capabilities is tax-aware optimization: it identifies unrealized losses for harvesting, helps offset gains, and reasons across account types to improve after-tax outcomes — work that has historically required a financial advisor and a CPA working together.
Why accuracy matters here: Portfolio recommendations without context are noise. By anchoring every suggestion to the user's specific goals and holdings — and grounding signal in the same rigorous scoring models from Stocks Assistant — the agent ensures relevance, not just directional correctness.
Financial Planner AI Assistant
Most people interact with a financial planner once a year, if that. Financial Planner AI Assistant changes that with a deceptively simple premise: financial planning should evolve with life, not sit in a drawer after an annual review.
The assistant monitors retirement readiness, tracks cash flow, balances savings goals against debt and insurance obligations, and surfaces the next financial action — whenever it matters. For financial institutions and fintech platforms, it delivers personalized planning experiences at scale without requiring a dedicated advisor per user.
Why accuracy matters here: Life financial decisions compound over decades. An assistant that gives gently wrong guidance can meaningfully damage a family's financial outcomes. DataKnobs designed this product to continuously reassess based on real inputs — not static snapshots.
E-commerce Analysis Agent
Financial analysis in e-commerce has suffered a specific dysfunction: teams have plenty of dashboards but too little understanding of why metrics are moving and what to do about it. The E-commerce Analysis Agent is built as an always-on business analyst — explaining causes, not just reporting numbers.
The agent ingests traffic, product views, cart activity, checkout data, purchases, and post-purchase usage signals, then analyzes the complete journey to identify friction points and growth levers. Critically, it closes the loop with financial recommendations: pricing adjustments, bundling opportunities, funnel interventions, and lifecycle changes grounded in revenue contribution and customer lifetime value.
Why accuracy matters here: E-commerce teams frequently optimize a leaky funnel metric without understanding downstream revenue impact. This agent connects behavior data to financial outcomes — ensuring recommendations are grounded in margin implications and customer LTV, not just click rates.
Tax Research Assistant
Tax research is among the most demanding applications for AI accuracy. The stakes are direct and verifiable: an incorrect answer costs money, triggers compliance risk, or produces advice a licensed professional cannot stand behind. DataKnobs built Tax Research Assistant with this constraint as its design center — not an afterthought.
Rather than a generic tax chatbot, the product is a structured workflow system. CPAs define the exact intake questions for each client scenario. The assistant collects complete context, converts W-2s, 1099s, and K-1s into normalized JSON, and routes each question through a profile-specific reasoning path. An optional RAG layer grounds answers in the firm's own internal tax guidance and reference content.
Why accuracy matters here: Generic LLM responses to tax questions are dangerous. By engineering a pipeline that first collects structured facts, then routes reasoning through a profile-specific workflow, DataKnobs has built a system where accuracy is architected — not hoped for.
The DataKnobs Engineering Philosophy
Structured Before Generative
Data is normalized and structured before it ever reaches the generative AI layer. Earnings transcripts become scoring models. Tax documents become JSON. This is where accuracy is built.
Context Before Response
Whether it's a CPA's intake questionnaire or an investor's portfolio profile, every agent grounds its recommendations in the specific context of the person asking — never generic best practices.
Signal, Not Noise
Scoring models, funnel analysis, and workflow routing all share one goal: compressing complex, noisy data into interpretable signals that support better decisions. The compression is the product.
Action, Not Just Insight
Every product closes the loop from analysis to recommendation. Users aren't left to translate a dashboard into a decision — the agent does that translation explicitly.
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