Software · Automation · AI
for Modern Enterprises
Software · Automation · AI
for Modern Enterprises
Alpha IQ is a reference architecture for building conversational AI over enterprise data. We've stress-tested it on Indian capital markets — multi-analyst research, backtesting, screening, document intelligence, and operational alerts across 3,700+ NSE and BSE-listed companies. The same pattern adapts to any structured-data domain.
Most "AI for X" tools hard-code one domain into the model layer. We took the opposite approach: a domain-agnostic agent runtime, with the schema, tools, and data sources as pluggable inputs.
The capital-markets pilot proves the framework on a particularly hard surface — structured tabular data, time-series, document corpora, and external APIs all in one workflow. The same architecture transfers to compliance, operations, supply chain, or any domain with messy structured data and analyst-style questions.
Grouped into five clusters. Each is a discrete capability your team can adopt piecemeal or take whole.
Talk to your data in plain language. The agent understands context, follows up across turns, and remembers your prior questions.
Three specialist agents — Technical, Fundamental, Sentiment — run in parallel. A Master Analyst reconciles their verdicts and surfaces disagreements.
Upload PDFs, spreadsheets, and reports — the agent extracts the data, indexes it, and reasons over it alongside your structured sources, citing sources back.
Single-instrument and portfolio-level backtests with Pine Script, MQL5, and Python code export. Pre-built strategy catalog plus user-authored strategies.
Multi-criteria SQL-based filters over the data lake. Includes the Minervini uptrend template, fundamental scoring, and user-authored screens.
DCF intrinsic value, CAPM, Monte Carlo, Kelly criterion, portfolio optimisation, and the standard suite of risk metrics.
Price, comparison, line, and bar charts rendered as PNG inline in agent responses or downloadable.
Real-time BSE snapshot — volume spikes, circuit hitters, sector breakdown, and breadth indicators.
Dividends, splits, AGMs, board meetings, insider trades. Filtered, scheduled, and pushed to monitors.
2,200+ NSE and 1,500+ BSE stocks. Intraday and tick-level price data, quarterly fundamentals, derivatives, fixed income, shareholding patterns — and clean, ambiguity-resolved tickers across exchanges.
Connects to the data you already have — wherever it lives. Lakes, warehouses, internal APIs, structured documents — all surface through the same agent interface.
Eight condition types — price, volume, fundamentals, news, custom SQL — evaluated continuously and delivered via WhatsApp or email.
Bi-directional chat with the agent, alert delivery, research dispatch, and full delivery tracking.
Agents can run on-the-fly calculations and modelling — safely isolated from your production systems. Numbers your team can trust, without security tradeoffs.
Pull holdings, positions, and trade history from brokerage accounts or document uploads. Portfolios become first-class context the agent reasons over.
A 52-term financial dictionary plus per-user research notes with full markdown — searchable, exportable, agent-accessible.
User-managed lists, taggable, runnable against any screen or strategy.
Cost transparency, skill breakdown, and a 50-query history. Surfaces what the agent is doing and what it costs.
Analytics dashboards, feedback tracking, the data scheduler, and an observability layer for ops teams.
The agent loads only the tools the current question actually needs, not the full catalog. The result is shorter response times, predictable token spend, and more capacity for the agent to reason through complex questions instead of drowning in setup.
For deep research, specialist agents — Technical, Fundamental, Sentiment in the capital-markets pilot — work in parallel and a Master Analyst reconciles their findings into a single verdict, citing sources back to the data lake. Where the specialists disagree, you see the disagreement. No false certainty.
When the agent needs to run a calculation that goes beyond a database query, it can — inside a sandbox that has no filesystem access, no network, and a tight time budget. Your production systems stay untouched. Your security team gets a story they can sign off.
The agent framework is the asset. The capital-markets pilot proves it works on the hardest variant of structured-data analysis. The same pattern adapts for compliance copilots, supply-chain analysis, and operations command-centre use cases — each with a different schema, different tools, and the same runtime.
If you have an analyst-style workflow over messy structured data — financial, operational, regulatory, or otherwise — we can adapt this architecture to your domain. The 4-step engagement model on the home page applies: discovery, MVP build, production, hand-off.
A short conversation to understand your data, your analyst workflows, and where conversational AI would actually move the needle. We start with discovery, not a sales pitch.